scholarly journals Forecasting of Kharif Rice and Jute Yield in North Bengal through Statistical Model

2021 ◽  
pp. 60-65
Author(s):  
Kousik Nandi ◽  
Anwesh Rai ◽  
Soumen Mondal ◽  
Subhendu Bandyopadhyay ◽  
Deb Sankar Gupta
Keyword(s):  
Statistical Model ◽  
Crop Yield ◽  
Climate Change Scenario ◽  
Rice Yield ◽  
State Level ◽  
Change Scenario ◽  
Yield Prediction ◽  
Yield Forecast ◽  
Crop Yield Forecasting ◽  
North Bengal

Crop yield forecasting under the present climate change scenario needs an effective model and its parameter that how crop respond to the weather variable. A number of weather based models have been developed to estimate the crop yield for the various crops at block, district and state level. Among the different model statistical model is more popular and commonly used. The current study was undertaken to evaluate the performance of statistical model for rice and jute yield forecast of four different district viz. Cooch Behar, Jalpaiguri, Uttar Dinajpurand and Dakhin Dinajpur. Among the four districts Cooch Behar district found superior for kharif rice yield prediction (1.46% error with RMSE 177.68 kg/ha) whereas in case of jute crop its performance was the best in the Jalpaiguri district (-0.44% error with RMSE 217.50 kg/ha).

scholarly journals Deep Neural Network Model for Proficient Crop Yield Prediction

2021 ◽  
Vol 309 ◽  
pp. 01031
Author(s):  
K. Pravallika ◽  
G. Karuna ◽  
K. Anuradha ◽  
V. Srilakshmi
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Knowledge Process ◽  
Fundamental Understanding ◽  
Yield Forecast ◽  
Learning Machine ◽  
Crop Yield Forecasting ◽  
And Storage

Crop yield forecasting mainly focus on the domain of agriculture research which has a great impact on making decisions like import-export, pricing and distribution of respective crops. Accurate predictions with well timed forecasts is very important and is a tremendously challenging task due to numerous complex factors. Mainly crops like wheat, rice, peas, pulses, sugarcane, tea, cotton, green houses etc. can be used for crop yield prediction. Climatic changes and unpredictability influence mainly on crop production and maintenance. Forecasting crop yield well before harvest time can help farmers for selling and storage. Agriculture deals with large datasets and knowledge process. Many techniques are there to predict the crop yield. Farmers are benefited commercially by these predictions. Factors such as Geno type, Environment, Climatic conditions and Soil types used in predicting the Yield. For predicting accurately we need to know the fundamental understanding and relationship between the interactive factors and the yield to reveal the relationships between the datasets which are comprehensive and powerful algorithms. Based on the study of various survey papers it has been found that in all the crop predictions, various deep learning, machine learning and ANN algorithms implemented to predict yield forecast and the results are analyzed.

Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil

2020 ◽  
Vol 284 ◽  
pp. 107886 ◽  
Author(s):  
Raí A. Schwalbert ◽  
Telmo Amado ◽  
Geomar Corassa ◽  
Luan Pierre Pott ◽  
P.V.Vara Prasad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Weather Data ◽  
Yield Prediction ◽  
Southern Brazil ◽  
Soybean Yield ◽  
Yield Forecast

scholarly journals Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4537
Author(s):  
Liyun Gong ◽  
Miao Yu ◽  
Shouyong Jiang ◽  
Vassilis Cutsuridis ◽  
Simon Pearson
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Environmental Parameters ◽  
Yield Prediction ◽  
Convolutional Network ◽  
Financial Decisions ◽  
The Arts ◽  
Comprehensive Evaluations ◽  
Crop Yield Forecasting ◽  
Mean Square Errors

Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.

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.

Soil salinity risk in a climate change scenario and its effect on crop yield

2020 ◽  
pp. 351-396
Author(s):  
Alexandra Tomaz ◽  
Patrícia Palma ◽  
Paula Alvarenga ◽  
Maria Conceição Gonçalves
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Soil Salinity ◽  
Climate Change Scenario ◽  
Change Scenario

Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S

2019 ◽  
Vol 234 ◽  
pp. 55-65 ◽  
Author(s):  
Yan Li ◽  
Kaiyu Guan ◽  
Albert Yu ◽  
Bin Peng ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Statistical Model ◽  
Crop Yield ◽  
Yield Prediction ◽  
Prediction Modeling ◽  
The U.S

scholarly journals Identifying most promising agronomic adaptation strategies to close rainfed rice yield gap in future: a model-based assessment

2021 ◽  
Author(s):  
Subhankar Debnath ◽  
Ashok Mishra ◽  
D. R. Mailapalli ◽  
N. S. Raghuwanshi
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Rice Yield ◽  
Negative Impact ◽  
Baseline Period ◽  
Adaptation Strategies ◽  
Change Scenario ◽  
Yield Gap ◽  
Rainfed Rice ◽  
Agronomic Adaptation

Abstract There is an increasing consensus that climate change may have a high negative impact on crop yield, and that it will affect farmers in developing and least developed counties the most. ‘Close the yield gap’ could be one of the promising options to address the issue of yield improvement. Better understanding of adaptation strategies and implication of the adaptations in crop yield are required to close the yield gap. In this study, the effectiveness of agronomic adaptation options on rainfed rice yield gap was evaluated for the baseline period (1981–2005) and two future periods (2016–2040 and 2026–2050) for India by using bias-corrected RegCM4 output and the Decision Support System for Agrotechnology Transfer (DSSAT) model. Results suggested that a combined adjustment of transplanting time (advancing by fortnight), crop spacing ((10 × 10) cm) and N-fertilizer application (140 kg/ha) was the best strategy as compared to single adaptation option to close the yield gap under the climate change scenario. The strategy improved rice yield by 37.5–168.0% and reduced average attainable yield gap among the cultivars from 0.74 to 0.16 t/ha under future climate projection. This study provides agronomic indications to rice growers and lays the basis for an economic analysis to support policy-makers, in charge of promoting the sustainability of the rainfed rice-growing systems.

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