scholarly journals Comparison of Lasso and stepwise regression technique for wheat yield prediction

2021 ◽  
Vol 21 (2) ◽  
pp. 188-192
Author(s):  
SUDHEER KUMAR ◽  
S.D. ATTRI ◽  
K.K. SINGH
Keyword(s):  
Crop Production ◽  
Stepwise Regression ◽  
Wheat Yield ◽  
Forecast Model ◽  
Regression Technique ◽  
Weather Data ◽  
Prediction Errors ◽  
Lasso Regression ◽  
Statistical Parameters ◽  
Forecast Models

Multiple regression approach has been used to forecast the crop production widely. This study has been undertaken to evaluate the performance of stepwise and Lasso (Least absolute shrinkage and selection operator) regression technique in variable selection and development of wheat forecast model for crop yield using weather data and wheat yield for the period of 1984-2015, collected from IARI, New Delhi. Statistical parameters viz. R2, RMSE, and MAPE were 0.81, 195.90 and 4.54 per cent respectively with stepwise regression and 0.95, 99.27, 2.7 percentage, respectively with Lasso regression. Forecast models were validated during 2013-14 and 2014-15. Prediction errors were -8.5 and 10.14 per cent with stepwise and 1.89 and 1.64 percent with the Lasso. This shows that performance of Lasso regression is better than stepwise regression to some extent.

WHEAT YIELDS FOR 50 YEARS AT SWIFT CURRENT, SASKATCHEWAN IN RELATION TO WEATHER

1974 ◽  
Vol 54 (4) ◽  
pp. 625-650 ◽  
Author(s):  
GEO. W. ROBERTSON
Keyword(s):  
Crop Production ◽  
Global Radiation ◽  
Wheat Yield ◽  
Triticum Aestivum L ◽  
Quadratic Functions ◽  
Weather Data ◽  
Temperature And Precipitation ◽  
Weather Model ◽  
Surveillance Programs ◽  
Crop Condition

Half a century of wheat yield and weather records at Swift Current in southwestern Saskatchewan were analyzed to determine the response of wheat (Triticum aestivum L.) to changing weather patterns. Weather at Swift Current has undergone subtle but significant changes over the past 50 yr. Earlier years had disturbed conditions: hot, dry periods alternating with cool, wet ones resulting in yield fluctuations ranging from crop failures to maximum values. More recently the weather has been quiet: dry and cool but less variable from year to year. The resulting conditions were more favorable for near-normal but less variable yields. Simple precipitation-based yield–weather models developed two decades ago no longer apply, because temperature and precipitation patterns are currently out of phase relative to earlier conditions. A factorial yield–weather model was used to explain the complex relationship. This involved the summation of the product of several quadratic functions of various weather elements. Those elements considered were precipitation, maximum and minimum temperatures, global radiation estimated from duration of bright sunshine, evaporation from a buried pan, and time as an indicator of advancing technology. One function contained a term for the antecedant crop condition. The most important elements were precipitation for the summer-fallow period and for May, June and August; maximum temperatures for June and July; and global radiation for May. Advances in technology would seem to have very little influence on wheat yield trends after weather trends were accounted for. The model accounted for 73% (r = 0.854) of the yield variability and provided realistic functions for explaining the curvilinear influence of individual weather elements on wheat yield. The model is of a form that is readily adaptable for assessing, at any time during the crop development period, the influence of past and current weather on future expected yield. This could be useful for interpreting weather data in terms of crop production in weather and crop condition surveillance programs.

Assessment of Wind and Wave High-Resolution Forecasts During High-Energy Weather Events in the Brazilian Coast

2021 ◽  
Author(s):  
F. M. Chagas ◽  
B. R. F. Rachid ◽  
B. G. Ambrosio ◽  
A. A. Luz ◽  
C. B. Gramcianinov ◽  
...  
Keyword(s):  
High Resolution ◽  
Complex Geometry ◽  
Forecast Model ◽  
High Energy ◽  
Brazilian Coast ◽  
Statistical Parameters ◽  
Wave Measurements ◽  
Weather Events ◽  
Forecast Models

Abstract We present a high-resolution metocean forecast model (Aimar), which provides 24/7 results for the Brazilian coast. The model integrates global model boundary conditions and detailed coastal models, especially for complex geometry areas near ports and major coastal cities. The aim of this paper is to assess the forecast reliability and to present model data compared to in-situ measurements under high energy weather events. Mean wind velocity and direction were investigated during the occurrence of an extratropical cyclone near Brazilian coast. The model has been assessed by comparing its results to two specific events, one for winds and one for waves. Results of the tested wind event show that Aimar results predict the high energy winds in advance of 5 days, while NCEP’s Global Forecast System Ensemble (GFSe) predicted the same event in advance of 2–3 days, for the region of Santos city. Results of the tested wave event show that Aimar forecasts properly represent the wave propagation for complex geometry coasts. The high-resolution coastal model could predict the nearshore state of sea agitation caused by the passage of a cold front. Model agreement with in-situ wave measurements adjacent to Rio de Janeiro-RJ city were considered Excellent and Good, according to statistical parameters R and RMAE. These results show that high-resolution coastal forecast models can be applied to increase the efficiency, resource uses and reduce the risks for marine operations and engineering works.

scholarly journals Effect of warming temperatures on US wheat yields

2015 ◽  
Vol 112 (22) ◽  
pp. 6931-6936 ◽  
Author(s):  
Jesse Tack ◽  
Andrew Barkley ◽  
Lawton Lanier Nalley
Keyword(s):  
Crop Production ◽  
Wheat Variety ◽  
Wheat Yield ◽  
Predictive Performance ◽  
Extreme Heat ◽  
Weather Data ◽  
Effect Of Temperature ◽  
Data Set ◽  
Freezing Temperatures ◽  
Temperature Exposure

Climate change is expected to increase future temperatures, potentially resulting in reduced crop production in many key production regions. Research quantifying the complex relationship between weather variables and wheat yields is rapidly growing, and recent advances have used a variety of model specifications that differ in how temperature data are included in the statistical yield equation. A unique data set that combines Kansas wheat variety field trial outcomes for 1985–2013 with location-specific weather data is used to analyze the effect of weather on wheat yield using regression analysis. Our results indicate that the effect of temperature exposure varies across the September−May growing season. The largest drivers of yield loss are freezing temperatures in the Fall and extreme heat events in the Spring. We also find that the overall effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Our analysis indicates that there exists a tradeoff between average (mean) yield and ability to resist extreme heat across varieties. More-recently released varieties are less able to resist heat than older lines. Our results also indicate that warming effects would be partially offset by increased rainfall in the Spring. Finally, we find that the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts on yields.

Exploratory Research on Earnings Forecast Model-Based on Chinese Manufacturing Companies

2012 ◽  
Vol 566 ◽  
pp. 580-583
Author(s):  
Lu Zhuang Wang ◽  
Zheng Yuan Wang ◽  
Zi Shu Yuan
Keyword(s):  
Financial Management ◽  
Stepwise Regression ◽  
Forecast Model ◽  
Listed Companies ◽  
Earnings Forecast ◽  
Exploratory Research ◽  
Financial Indicators ◽  
Manufacturing Companies ◽  
Forecast Models ◽  
Earnings Forecasting

This paper takes China's manufacturing listed companies as an example, applies matching T test to select key financial indicators, uses factor analysis to determine the main factors, and explores company earnings forecast models by comparing different kinds of stepwise regression plans. Empirical results showed that by means of properly linear combination of key financial indicators from this year, it is possible to estimate the earning, EPS, for next year. The accuracy of the forecast can reach 80% for Top 10 profitable enterprises, and 62% for Top 100, which validated the effectiveness of this exploration for earnings forecasting. Finally, based on the prediction model, the authors put forward some suggestions for improving financial management of listed companies.

scholarly journals Different approaches on pre harvest forecasting of wheat yield

2015 ◽  
Vol 7 (2) ◽  
pp. 839-843
Author(s):  
Y. A. Garde ◽  
B. S. Dhekale ◽  
S. Singh
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Function Analysis ◽  
Wheat Yield ◽  
National Income ◽  
Uttar Pradesh ◽  
Forecast Model ◽  
Wheat Crop ◽  
Yield Forecast ◽  
Rabi Crops

Agriculture is backbone of Indian economy, contributing about 40 per cent towards the Gross National Product and provide livelihood to about 70 per cent of the population. According to the national income published in Economic survey 2014-15, by the CSO, the share of agriculture in total GDP is 18 percent in 2013-14. The Rabi crops data released by the Directorate of Economics and Statistics recently indicates that the total area coverage has declined; area under wheat has gone down by 2.9 per cent. Therefore needs to be do research to study weathersituation and effect on crop production. Pre harvest forecasting is true essence, is a branch of anticipatory sciences used for identifying and foretelling alternative feasible future. Crop yield forecast provided useful information to farmers, marketers, government agencies and other agencies. In this paper Multiple Linear Regression (MLR) Technique and discriminant function analysis were derived for estimating wheat productivity for the district of Varanasi in eastern Uttar Pradesh. The value of Adj. R2 varied from 0.63 to 0.94 in different models. It is observed that high value of Adj. R2 in the Model-2 which indicated that it is appropriate forecast model than other models, also the value of RMSE varied from minimum 1.17 to maximum 2.47. The study revealed that MLR techniques with incorporating technical and statistical indicators (Model 2) was found to be better for forecasting of wheat crop yield on the basis of both Adjusted R2 and RMSE values.

scholarly journals Fungicide Application Affects Nitrogen Utilization Efficiency, Grain Yield, and Quality of Winter Wheat

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1295
Author(s):  
Ahossi Patrice Koua ◽  
Mirza Majid Baig ◽  
Benedict Chijioke Oyiga ◽  
Jens Léon ◽  
Agim Ballvora
Keyword(s):  
Synergistic Effect ◽  
Grain Yield ◽  
Crop Production ◽  
Agronomic Traits ◽  
Yellow Rust ◽  
Wheat Yield ◽  
Utilization Efficiency ◽  
High Nitrogen ◽  
Available N ◽  
Fungicide Application

Nitrogen (N) is a vital component of crop production. Wheat yield varies significantly under different soil available N. Knowing how wheat responds to or interacts with N to produce grains is essential in the selection of N use efficient cultivars. We assessed in this study variations among wheat genotypes for productivity-related traits under three cropping systems (CS), high-nitrogen with fungicide (HN-WF), high-nitrogen without fungicide (HN-NF) and low-nitrogen without fungicide (LN-NF) in the 2015, 2016 and 2017 seasons. ANOVA results showed genotypes, CS, and their interactions significantly affected agronomic traits. Grain yield (GY) increased with higher leaf chlorophyll content, importantly under CS without N and fungicide supply. Yellow rust disease reduced the GY by 20% and 28% in 2015 and 2016, respectively. Moreover, averaged over growing seasons, GY was increased by 23.78% under CS with N supply, while it was greatly increased, by 52.84%, under CS with both N and fungicide application, indicating a synergistic effect of N and fungicide on GY. Fungicide supply greatly improved the crop ability to accumulate N during grain filling, and hence the grain protein content. Recently released cultivars outperformed the older ones in most agronomic traits including GY. Genotype performance and stability analysis for GY production showed differences in their stability levels under the three CS. The synergistic effect of nitrogen and fungicide on grain yield (GY) and the differences in yield stability levels of recently released wheat cultivars across three CS found in this study suggest that resource use efficiency can be improved via cultivar selection for targeted CS.

Nitrogen form, time and rate of application, and nitrification inhibitor effects on crop production

2014 ◽  
Vol 94 (2) ◽  
pp. 425-432 ◽  
Author(s):  
R. E. Karamanos ◽  
K. Hanson ◽  
F. C. Stevenson
Keyword(s):  
Crop Production ◽  
Protein Concentration ◽  
Wheat Yield ◽  
Nitrification Inhibitor ◽  
N Fertilizer ◽  
Nitrogen Form ◽  
Anhydrous Ammonia ◽  
N Concentration ◽  
Fertilizer Rates ◽  
Better Than

Karamanos, R., Hanson, K. and Stevenson, F. C. 2014. Nitrogen form, time and rate of application, and nitrification inhibitor effects on crop production. Can. J. Plant Sci. 94: 425–432. Nitrogen management options for anhydrous ammonia (NH3) and urea were compared in a barley–wheat–canola–wheat cropping sequence (2007–2010) at Watrous and Lake Lenore, SK. The treatment design included a factorial arrangement of N fertilizer form (NH3versus urea), nitrification inhibitor application, time of N application (mid-September, mid- to late October, and spring) and four N fertilizer rates (0, 40, 80 and 120 kg ha−1). Anhydrous ammonia applications at 40 kg N ha−1in 2008 (fall) and in 2010 (all times of application) resulted in wheat yield reductions relative to the same applications for urea. For wheat years, yield was reduced for both fall versus spring N fertilizer applications, when no nitrification inhibitor was applied and the inclusion of nitrification inhibitor maintained wheat yield at similar levels across all times of N fertilizer applications, regardless of form. Protein concentration was approximately 2 g kg−1greater with urea compared with NH3at both sites in 2008 and only at Watrous in 2010. Also, early versus late fall N fertilizer applications consistently increased N concentration of grain only for the 40 and/or 80 kg N ha−1rates. Effects of nitrification inhibitor on N concentration were not frequent and appeared to be minimal. Urea had greater agronomic efficiency (AE) than NH3at the lower N fertilizer rates. The nitrification inhibitor had a positive effect on wheat AE only for early fall N fertilizer applications. It can be concluded that for maximum yields NH3or urea will be suitable if applied at rates of 80 kg N ha−1and greater. If N fertilizer is applied at 40 kg N ha−1, especially in fall without inhibitor, urea is better. In terms of protein concentration for wheat, urea seemed to better than NH3and fall was better than spring application.

scholarly journals Sensitivities of the Madden–Julian oscillation forecasts to configurations of physics in the ECMWF global model

2021 ◽  
Vol 21 (6) ◽  
pp. 4759-4778
Author(s):  
Jun-Ichi Yano ◽  
Nils P. Wedi
Keyword(s):  
Forecast Model ◽  
Global Model ◽  
Madden Julian Oscillation ◽  
Forecast Performance ◽  
Theoretical Investigations ◽  
Forecast Models ◽  
Forecasting System ◽  
The Rich ◽  
The Tropics ◽  
Original Motivation

Abstract. The sensitivities of the Madden–Julian oscillation (MJO) forecasts to various different configurations of the parameterized physics are examined with the global model of ECMWF's Integrated Forecasting System (IFS). The motivation for the study was to simulate the MJO as a nonlinear free wave under active interactions with higher-latitude Rossby waves. To emulate free dynamics in the IFS, various momentum-dissipation terms (“friction”) as well as diabatic heating were selectively turned off over the tropics for the range of the latitudes from 20∘ S to 20∘ N. The reduction of friction sometimes improves the MJO forecasts, although without any systematic tendency. Contrary to the original motivation, emulating free dynamics with an operational forecast model turned out to be rather difficult, because forecast performance sensitively depends on the specific type of friction turned off. The result suggests the need for theoretical investigations that much more closely follow the actual formulations of model physics: a naive approach with a dichotomy of with or without friction simply fails to elucidate the rich behaviour of complex operational models. The paper further exposes the importance of physical processes other than convection for simulating the MJO in global forecast models.

scholarly journals Wheat yield prediction in relation to climatic parameters using statistical model for Ludhiana district of central Punjab

2021 ◽  
Vol 23 (1) ◽  
pp. 122-126
Author(s):  
MAHESH CHAND SINGH ◽  
VAJINDER PAL ◽  
SOM PAL SINGH ◽  
SANJAY SATPUTE
Keyword(s):  
Crop Production ◽  
Production Management ◽  
Wheat Yield ◽  
Climatic Conditions ◽  
Wheat Crop ◽  
Climatic Data ◽  
Strong Positive Correlation ◽  
Policy Makers ◽  
Production Management System ◽  
Main Determinants

Climate change which is one of the main determinants of agricultural production has started affecting the crop growth pattern and yield from past couple of decades in various agro-climatic zones globally. Under such scenario, the prior forecasting of yield of field crops such as wheat via modeling techniques can help in simplifying the crop production management system starting from farmer’s level to policy makers. The present study was thus undertaken to model the wheat yield of Ludhiana district of  Indian Punjab through regression analysis of historical data (1993-2017) of wheat yield and climatic conditions in the area. The developed model was successfully validated with a strong positive correlation (R2=0.81) between predicted and observed data. Both observed and predicted yields were having similar trend with a minimum and maximum absolute differential error of 0.1 and 13.9% respectively. The developed model may serve as a powerful tool for predicting the future yield of wheat crop with available futuristic climatic data of the study area.

scholarly journals Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer
Keyword(s):  
High Resolution ◽  
Crop Production ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Model ◽  
Stress Index ◽  
Yield Prediction ◽  
Field Scale ◽  
Crop Water Stress Index ◽  
Sentinel 2

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.

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