Performance evaluation of yield crop forecasting models using weather index regression analysis (original) (raw)
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Concept of the paper is firstly to remove the trend of crop yield and then to develop the forecasting models using detrended yield. Not much work is available or development of forecast models or modelling due to their non-linear behaviour. For that, in this paper, methodology developed for forecasting using nonlinear growth models, which will help in forecasting yield, pest and disease incidences etc with high accuracy. Crop yield forecast models for wheat crop have been developed (using non-linear growth models, linear models and weather indices approach with weekly weather data) for different districts of Uttar Pradesh (UP). Weather Indices (WI) were obtained using above two approaches. Weather indices based regression models were developed using weather indices as independent variables while character under study such as crop yield was used as dependent variable for wheat crop, i.e. two step non-linear forecast model. Technique of forecasting using non-linear approach and using ...
Modelling and forecasting of wheat yield data based on weather variables
Indian Journal of Agricultural Sciences
Forecasting of crop yield based on historical data and pertinent external climatic information is considered. To this end, Autoregressive Integrated Moving Average with Exogenous variables (ARIMAX) time-series model along with its estimation procedure is studied. In the present investigation, five models at five important stages of wheat growth are developed by including the most important weather variables. The weekly maximum temperature at crown root initiation (CRI) stage, tillering stage, anthesis stage, milk stage and dough stage and evapotranspiration at CRI stage are used for model development. As an illustration, ARIMAX models are employed for forecasting of wheat yield in Kanpur district of Uttar Pradesh. Comparative study of the fitted models is carried out from the viewpoint of Relative mean absolute prediction error (RMAPE). It is demonstrated that the ARIMAX methodology is able to provide pre-harvest forecasts based on weather variables at various stages of wheat crop growth, starting from CRI stage (21 days after sowing) to dough stage (126 days after sowing). It is observed that, as wheat crop grows towards maturity, pre-harvest forecasts get closer to actual values.
Pre harvest forecasting of crop yield using non-linear regression modelling: A concept
The Indian Journal of Agricultural Sciences
The concept of pre-harvesting of crop yield using nonlinear growth models and detrended yield for developing yield forecast model is rarely employed in forecasting. A novel approach attempted in this study to use nonlinear models with different weather variables and their indices and compare them to identify a suitable forecasting model. Weather indices based regression models were developed using weather indices as independent variables whiledetrended yield (residuals) was considered as dependent variable. The approach provided reliable yield forecastabout two months before harvest.
WHEAT YIELD PREDICTION USING WEATHER BASED STATISTICAL MODEL IN NORTHERN ZONE OF HARYANA
The study was undertaken to investigate the impact of weather variables on crop productivity of wheat. Principal components of the weather parameters spread over the crop growth period were employed to forecast wheat yield(s) in northern zone (Ambala, Yamuna Nagar and Kurukshetra districts) of Haryana. Zonal weather models gave the desired predictive accuracy and provided a considerable improvement in the district-level wheat yield prediction. The results indicate the possibility of district-level wheat yield prediction, 4-5 weeks ahead of the harvest time, in Haryana.
International Journal of Environment and Climate Change, 2021
Twenty five year (1992 to 2017) of weather data of wheat, mustard and potato crop for 11 districts Aligarh, Baghpat, Barielly, Bijnor, Bulandshahar, Gaziabad, Meerut, Muzaffarnagar, Rampur, Saharnpur and Sahjahnpur of Western Uttar Pradesh were used to develop pre harvest yield prediction model. Every year Agromet Field Unit (AMFU) Modipuram generate district level yield forecasting model for major crops (wheat, mustard and poatato) pre-harvest stage (F3) for the seasons i.e. rabi. Considering the importance of wheat, mustard and potato crop a attempt was made to develop pre harvest yield forecasting models, in the selected 11 districts of western Uttar Pradesh. The models were validated with 2015 and 2016 data set. The results revealed that per harvest for forecasting model had F3 stage R2 values between 0.44 to 0.96 per cent for wheat crop , 0.57 to 0.87 per cent for mustard crop to and 0.54 and 0.99 per cent for Potato crop in the different districts of western Uttar-Pradesh. Dur...
Different approaches on pre harvest forecasting of wheat yield
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 weather situation 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. R 2 varied from 0.63 to 0.94 in different models. It is observed that high value of Adj. R 2 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 R 2 and RMSE values.
The Indian Journal of Agricultural Sciences
A study was carried out to forecast the yield of the wheat crop for five districts of Uttar Pradesh namely Lucknow, Kanpur, Banda, Jhansi and Faizabad. The daily weather data on variables such as maximum temperature, rainfall, minimum temperature, and relative humidity were arranged week wise from sowing to harvesting and the relations between the weather variables and yield was worked out using statistical tools like correlation and regression. The yield has been detrended by obtaining the parameter estimates of the model and subsequently the detrended yield was used to forecast the yield of the crop using ARIMA model. The proposed method of obtaining pre-harvest forecasting of yield of crops was compared with the traditional approaches of forecasting and the proposed method was evaluated in terms of criteria's such as goodness of fit of the model. It was observed that in all the districts the proposed model performed better as compared to the traditional method both in terms o...
Journal of Agrometeorology, 2021
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...
PRE-HARVEST WHEAT YIELD FORECAST THROUGH AGRO-METEOROLOGICAL INDICES FOR NORTHERN REGION OF HARYANA
Parameter estimation in statistical modeling plays a crucial role in the real world phenomena. Several alternative analyses may be required for the purpose. An attempt has been made in this paper to assess the impact of weather variables for district-level wheat yield estimation in the Northern region (Haryana). Phase wise weather data and trend based yield was used for developing the zonal trend-agro meteorological (agromet) models within the framework of multiple linear regression and principal components analysis. The results indicate the possibility of district-level wheat yield prediction, 4-5 weeks ahead of the harvest time. Zonal weather models had the desired predictive accuracy and provided considerable improvement in the district-level wheat yield estimates. The principal component analysis offers a considerable improvement over least squares method in the presence of multicollinearity. The estimated yield(s) from the selected models indicated good agreement with State Department of Agriculture (DOA) wheat yields by showing 2-10 percent average absolute deviations in most of the districts except for the Panchkula district.
Multivariate statistical wheat yield prediction model for Bilaspur district of Chhattisgarh
2018
Weather plays an important role in yield forecasting of agricultural crop. Weather affects crop growth at different phenological phases and is therefore, responsible for variation in yields from year-to-year and place-to-place. An attempt has been made in this paper to study the effect of vital weather parameters on wheat yield on basis of 15 years (2000-2015) weather data and wheat production and to develop a different type of multivariate statistical model for yield forecast of this region. Different types of models have been developed using SPSS software. Model 5 has the highest R2 value 0.97, which describes the 97% variability in wheat yield due to weather parameters.