scholarly journals Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 39-44 ◽  
Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Biophysical Model ◽  
Wheat Crop ◽  
Biophysical Parameters ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

scholarly journals Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4013 ◽  
Author(s):  
Dong Han ◽  
Shuaibing Liu ◽  
Ying Du ◽  
Xinrui Xie ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Content ◽  
Vegetation Indices ◽  
Spectral Feature ◽  
Wheat Crop ◽  
Crop Water ◽  
Inversion Model ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Sentinel 2

This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

scholarly journals Use of ordinal logistic regression in crop yield forecasting

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 913-918
Author(s):  
VANDITA KUMARI ◽  
RANJANA AGRAWAL ◽  
AMRENDER KUMAR
Keyword(s):  
Logistic Regression ◽  
Crop Yield ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Ordinal Logistic Regression ◽  
Percentage Error ◽  
Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.  

scholarly journals Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
William W. Guo ◽  
Heru Xue
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Crop Yield ◽  
Wheat Yield ◽  
Network Models ◽  
High Accuracy ◽  
Neural Network Models ◽  
Yield Forecasting ◽  
Crop Forecasting ◽  
Crop Yield Forecasting

Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

scholarly journals Winter Wheat Response to Preplant Applications of Aminocyclopyrachlor

2011 ◽  
Vol 25 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Andrew R. Kniss ◽  
Drew J. Lyon
Keyword(s):  
Winter Wheat ◽  
Yield Loss ◽  
Wheat Yield ◽  
Field Studies ◽  
Wheat Crop ◽  
Yield Losses ◽  
Fallow Period ◽  
Winter Wheat Yield ◽  
Winter Wheat Crop ◽  
Preceding Winter

Field studies were conducted in Wyoming and Nebraska in 2007 through 2009 to evaluate winter wheat response to aminocyclopyrachlor. Aminocyclopyrachlor was applied at rates between 15 and 120 g ai ha−1 6, 4, and 2 mo before winter wheat planting (MBP). Redroot pigweed control was 90% with aminocyclopyrachlor rates of 111 and 50 g ha−1 when applied 4 or 2 MBP. Aminocyclopyrachlor at 37 g ha−1 controlled Russian thistle 90% when applied 6 MBP. At Sidney, NE, winter wheat yield loss was > 10% at all aminocyclopyrachlor rates when applied 2 or 4 MBP, and at all rates > 15 g ha−1 when applied 6 MBP. At Lingle, WY, > 40% winter wheat yield loss was observed at all rates when averaged over application timings. Although the maturing wheat plants looked normal, few seed were produced in the aminocyclopyrachlor treatments, and therefore preharvest wheat injury ratings of only 5% corresponded to yield losses ranging from 23 to 90%, depending on location. The high potential for winter wheat crop injury will almost certainly preclude the use of aminocyclopyrachlor in the fallow period immediately preceding winter wheat.

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 Effect of the Dose and Correlation of Organic and Mineral Fertilizers on Nitrogen Uptake and Winter Wheat Crop Yield in the Waterless Soil of the Mountain Shirvan

2020 ◽  
Vol 6 (4) ◽  
pp. 198-204
Author(s):  
S. Veliyeva
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Grain Quality ◽  
High Yield ◽  
Wheat Crop ◽  
Mineral Fertilizers ◽  
Net Income ◽  
Organic And Mineral Fertilizers ◽  
Winter Wheat Crop

Infield experiments on light chestnut soils of Mountain Shirvan, the effect of the dose and the ratio of organic and mineral fertilizers on nitrogen removal from aboveground biomass, grain quality, crop yield and economic efficiency in winter wheat crops were studied. The work was carried out in 2011–2014. During the work, standard methods for the determination of chemicals were used. As a result, the work is established. That high yield and net income were obtained in option N90P60K60.

Causes and implications of the unforeseen 2016 extreme yield loss in France’s breadbasket

2020 ◽  
Author(s):  
Tamara Ben Ari
Keyword(s):  
Crop Yield ◽  
Yield Loss ◽  
Root Zone ◽  
Wheat Yield ◽  
Grain Filling ◽  
Impact Modelling ◽  
Compound Events ◽  
Yield Forecasting ◽  
Crop Yield Forecasting ◽  
The Impact

<p>The 2016 wheat harvest in France suffered from an unforeseen and unprecedented production loss. At 5.4 tonnes ha<sup>-1</sup>, wheat yield was the lowest recorded since 1986 and 30% below the five-year average.  Crop yield forecasting can be considered as near-real-time impact modelling, but unfortunately, none of the forecasting systems in place anticipated the extent of the impact. The 2015/2016 growing season was characterized by compounding warm autumn temperatures and abnormally wet conditions in the following spring. High rainfall and high temperatures leading to fungal diseases, soil water lodging and anoxia, low radiation affecting grain filling, and leaching of nitrogen from the root-zone have all been suggested as important factors ultimately leading to the yield loss. The use of binomial logistic regressions accounting for autumn and spring temperatures and precipitation, suggests that the odds of an extreme yield loss in 2016 was times 35 higher than expected. The challenge now is to further identify the variety of biotic and abiotic processes interacting at different timescales. Collecting relevant insights on the field or from trial experiments, and confronting these with statistical and biophysical crop modelling will be key to achieve this. Improved impact relevant indicators will need to be integrated into operational crop yield forecasting systems in preparation for future compound events.</p>

scholarly journals Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 19
Author(s):  
Jiří Mezera ◽  
Vojtěch Lukas ◽  
Igor Horniaček ◽  
Vladimír Smutný ◽  
Jakub Elbl
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Nitrogen Fertilizers ◽  
Wheat Crop ◽  
Significant Difference ◽  
Winter Wheat Crop ◽  
The Individual ◽  
Sentinel 2

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

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