Mining effect of Temperature and Rainfall to develop an Empirical Model for Wheat Yield Prediction

2020 ◽  
Vol 13 ◽  
Author(s):  
Kanwal Preet Singh Attwal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Crop Yield ◽  
Meteorological Factors ◽  
Wheat Yield ◽  
Effect Of Temperature ◽  
Yield Prediction ◽  
Total Rainfall ◽  
Yield Data ◽  
Average Temperature ◽  
Whole Wheat ◽  
Maximum Variance

Background: Crop yield is affected by several agronomic factors such as soil type and date of sowing, and meteorological factors such as temperature and rainfall. While the agronomic factors are responsible for inter-region variations in yield, the year-wise yield variation in a particular region may be attributed to meteorological factors. Various Data Mining Techniques can be applied to analyse the effect of these factors on crop yield. Objective: To develop a model for prediction of Block-wise average wheat yield in Patiala district of Punjab, India. Method: Sampling is used for the collection of the yield data, and the data concerning temperature and rainfall is obtained from Indian Meteorological Department, Pune. The data is then pre-processed and analysed to study the effect of phase-wise average temperature and total rainfall on the wheat yield. The factors that are found to significantly affect yield are used for building a model for yield prediction. Results: It is found that the average temperature and the total rainfall for the whole wheat growing season are not much of help in explaining the variations in yearly wheat yield. The temperature and rainfall have different effects at different stages of plant growth and the yield is affected accordingly. It is inferred that that the average temperature and the total rainfall during the vegetative phase, and the grain development and ripening phase are the most important parameters for prediction of wheat yield. Conclusion: Stepwise selection mechanism is used to choose the variables whose inclusion explains the maximum variance in yield. The model is evaluated based on different parameters and is found to explain 95.6% of the yearly variations in yield.

scholarly journals Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

2021 ◽  
Author(s):  
Amit Kumar Srivast ◽  
Nima Safaei ◽  
Saeed Khaki ◽  
Gina Lopez ◽  
Wenzhi Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Learning Models ◽  
Winter Wheat Yield ◽  
Machine Learning Models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

Improved weather indices based Bayesian regression model for forecasting crop yield

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 879-886
Author(s):  
M. YEASIN ◽  
K. N. SINGH ◽  
A. LAMA ◽  
B. GURUNG
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Crop Yield ◽  
Wheat Yield ◽  
Bayesian Framework ◽  
Bayesian Regression ◽  
Estimation Of Parameters ◽  
Yield Data ◽  
Conjugate Priors ◽  
Modern Era

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The Markov chain Monte Carlo (MCMC) methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under Bayesian framework provided superior results as compared to the models estimated under the classical approach.

Wheat yield as influenced by urea applied with nitrification inhibitor and gibberellic acid

2020 ◽  
Author(s):  
Rayehe Mirkhani ◽  
Mehdi Shorafa ◽  
Mohammad Zaman
Keyword(s):  
Grain Yield ◽  
Crop Yield ◽  
Stem Elongation ◽  
Block Design ◽  
Wheat Yield ◽  
Nitrification Inhibitor ◽  
Growth And Yield ◽  
Control Treatment ◽  
Yield Data ◽  
Economic Implications

<p>Among the essential plant nutrients, nitrogen (N) is the most needed. Farmer apply N fertilizer, predominantly urea to meet crop N demand. However, a greater proportion of the applied urea-N is not being used by plants and lost to the atmosphere as ammonia or greenhouse gases. Therefore, it is necessary to enhance N use efficiency (NUE) of applied urea by minimizing such losses, which has environmental and economic implications. Nitrification inhibitor, such as nitrapyrin (NP), has the most potential to minimise N losses and enhance crop yield. Similarly, plant hormones, such as GA3, has the potential to reduce abiotic stress and improve plant growth and yield.   <br>A field experiment was established on an arable site at University of Tehran, Karaj to determine the effect of urea applied with Nitrapyrin and GA3 on wheat yield in 2018-2019. Karaj has a Mediterranean climate with annual precipitation of 265 mm. A randomized complete block design in five replications was used in this study. Treatments were: T1 (control treatment - without urea), T2 (farmers practice - 138 kg N/ha), and T3 (best practice - 138 kg N/ha+NP+GA3). Urea was applied in three split applications (46 kg N/ha) at growth stage (GS 21) or tillering, (GS 32) or stem elongation, and (GS 40) or booting. GA3 in T3 treatment, was applied only at stem elongation stage. <br>The crop yield data showed that, urea applied with NP and GA3 had a significant (p ≤ 0.01) effect on grain yield, biological yield, number of grains, 1000-grain weight and % Harvest Index (%HI) compared to other treatments. Urea applied with NP and GA3 increased grain yield (10.30 t ha-1) by 13.9% and 46.1% compared to farmer practices (9.04 t ha-1) and control treatment (7.05 t ha-1). These results suggest that co-application of urea with NP and GA3 has the potential to enhance wheat yield in semi-arid area of Iran.</p>

scholarly journals Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12602
Author(s):  
Yu Feng ◽  
Wen Lin ◽  
Shaobo Yu ◽  
Aixia Ren ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Meteorological Factors ◽  
Wheat Yield ◽  
Control Variable ◽  
Northern China ◽  
Small Error ◽  
Soil Water Storage ◽  
Growth Stages ◽  
Yield Prediction ◽  
Fallow Period

In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

scholarly journals A retrospective study: meteorological factors and COVID-19

2020 ◽  
Author(s):  
Cai Chen ◽  
Xiyuan Li ◽  
Xiangwei Meng ◽  
Zhixiang Ma ◽  
Wei Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Correlation Coefficient ◽  
Meteorological Factors ◽  
Rank Correlation ◽  
Effect Of Temperature ◽  
Maximal Effect ◽  
Global Epidemic ◽  
Average Temperature ◽  
Daily Average ◽  
Daily Average Temperature

Abstract Background: With the outbreak of novel coronavirus, the global epidemic prevention form is severe. Purpose: This paper aimed to investigate the association between meteorological factors (temperature, precipitation and relative humidity) and the daily new cases in Wuhan. Methods: generalized linear model was built to evaluate the link between daily average temperature and the new cases COVID-19. Spearman rank correlation coefficient was used to investigate the association between temperature, relative humidity, precipitation and the daily new cases COVID-19. Result: The correlation coefficient for daily average temperature, relative humidity, precipitation and NCP were 0.11, -0.083 and 0.17, respectively. The maximal effect of temperature on the new cases NCP appeared on Lag0. Conclusion: The variation of temperature had an effect on the daily new cases.

scholarly journals Operational semi-physical spectral-spatial wheat yield model development

2014 ◽  
Vol XL-8 ◽  
pp. 977-982 ◽  
Author(s):  
R. Tripathy ◽  
K. N. Chaudhary ◽  
R. Nigam ◽  
K. R. Manjunath ◽  
P. Chauhan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Wheat Yield ◽  
State Level ◽  
Radiation Use Efficiency ◽  
Wheat Crop ◽  
Yield Prediction ◽  
Use Efficiency ◽  
Yield Forecasting ◽  
Radiation Use

Spectral yield models based on Vegetation Index (VI) and the mechanistic crop simulation models are being widely used for crop yield prediction. However, past experience has shown that the empirical nature of the VI based models and the intensive data requirement of the complex mechanistic models has limited their use for regional and spatial crop yield prediction especially for operational use. The present study was aimed at development of an intermediate method based on the use of remote sensing and the physiological concepts such as the photo-synthetically active solar radiation (PAR) and the fraction of PAR absorbed by the crop (fAPAR) in Monteith’s radiation use efficiency based equation (Monteith, 1977) for operational wheat yield forecasting by the Department of Agriculture (DoA). Net Primary Product (NPP) has been computed using the Monteith model and stress has been applied to convert the potential NPP to actual NPP. Wheat grain yield has been computed using the actual NPP and Harvest index. Kalpana-VHRR insolation has been used for deriving the PAR. Maximum radiation use efficiency has been collected from literature and wheat crop mask was derived at MNCFC, New Delhi using RS2-AWiFS data. Water stress has been derived from the Land Surface Water Index (LSWI) which has been derived periodically from the MODIS surface reflectance data (NIR and SWIR1). Temperature stress has been derived from the interpolated daily mean temperature. Results indicated that this model underestimated the yield by 3.45 % as compared to the reported yield at state level and hence can be used to predict wheat yield at state level. This study will be able to provide the spatial wheat yield map, as well as the district-wise and state level aggregated wheat yield forecast. It is possible to operationalize this remote sensing based modified Monteith’s efficiency model for future yield forecasting with around 0.15 t ha-1 RMSE at state level.

scholarly journals Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lung-Chang Chien ◽  
Francisco Sy ◽  
Adriana Pérez
Keyword(s):  
At Risk ◽  
High Risk ◽  
Zika Virus ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Zikv Infection ◽  
Total Rainfall ◽  
Average Temperature ◽  
Risk Areas

Abstract Background Several Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations. Methods This study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model. Results When multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature. Conclusion Our study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.

scholarly journals Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

2020 ◽  
Vol 12 (2) ◽  
pp. 236 ◽  
Author(s):  
Jichong Han ◽  
Zhao Zhang ◽  
Juan Cao ◽  
Yuchuan Luo ◽  
Liangliang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Prediction ◽  
Climate Data ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

scholarly journals Improved weather indices based Bayesian regression model for forecasting crop yield

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 879-886
Author(s):  
M. YEASIN ◽  
K. N. SINGH ◽  
A. LAMA ◽  
B. GURUNG
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Crop Yield ◽  
Wheat Yield ◽  
Bayesian Framework ◽  
Bayesian Regression ◽  
Estimation Of Parameters ◽  
Yield Data ◽  
Conjugate Priors ◽  
Modern Era

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The Markov chain Monte Carlo (MCMC) methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under Bayesian framework provided superior results as compared to the models estimated under the classical approach.

scholarly journals May spring seasonal forecasts improve a climate service providing cereal yield predictions?

2021 ◽  
Author(s):  
Maria Nieves Garrido ◽  
J. Ignacio Villarino ◽  
Eroteida Sánchez ◽  
Inmaculada Abia ◽  
Marta Dominguez ◽  
...  
Keyword(s):  
Crop Yield ◽  
Growth Model ◽  
Wheat Yield ◽  
Crop Growth ◽  
Seasonal Forecasts ◽  
Crop Growth Model ◽  
Yield Data ◽  
Winter Climate ◽  
Winter Cereal ◽  
Climate Service

<p>Following the need of winter cereal farmers from the main producing region (Castilla y León) in Spain to estimate crop yield with at least one season of anticipation, we have developed a climate service based essentially on current and historical meteorological observations, on spring seasonal forecasts from ECMWF System 5 and on the crop growth model AquaCrop. Different experiments have been designed to produce both a synthetic yield database serving as observed truth and three different seasonal forecasting strategies. Calculation of objective verification scores for deterministic and probabilistic crop yield forecasts -including an assessment of their potential economic value- in hindcast mode determines the quality of this service and the differences among forecasting strategies. We demonstrate that the three compared strategies show good skill of wheat yield forecasts at the beginning of July, although the meteorological forcing for Aquacrop simulations between 1st April and 30th June is very different for the three compared strategies. The important role of the memory from previous (autumn and winter) climate conditions carried by the crop growth model is analysed and discussed.  A yearly assessment also allows some preliminary estimation of the value and possible benefits of the service for final users. Finally, we conclude that the simulation synthetically producing the observed truth compares rather well –especially the interannual variability- with other yield data based on surveys and experts estimations although it overestimates yield. Users have played a decisive role in co-design and co-development phases of this climate service. They have also actively intervened in the analysis and evaluation of results.</p><p> </p>

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