Integrating the impact of wheat fungal diseases in the Belgian crop yield forecasting system (B-CYFS)

2012 ◽  
Vol 40 ◽  
pp. 8-17 ◽  
Author(s):  
Moussa EL Jarroudi ◽  
Louis Kouadio ◽  
Martin Bertrand ◽  
Yannick Curnel ◽  
Frédéric Giraud ◽  
...  
Keyword(s):  
Crop Yield ◽  
Fungal Diseases ◽  
Forecasting System ◽  
Yield Forecasting ◽  
Crop Yield Forecasting ◽  
The Impact

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 Target food security: assimilating ultra-high resolution satellite images into a crop-yield forecasting model

2021 ◽  
Author(s):  
Matteo G. Ziliani ◽  
Bruno Aragon ◽  
Trenton Franz ◽  
Ibrahim Hoteit ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Satellite Images ◽  
Regional Scale ◽  
Area Index ◽  
High Resolution Satellite Images ◽  
Yield Forecasting ◽  
Crop Yield Forecasting ◽  
The Impact

<p>Assimilating biophysical metrics from remote sensing platforms into crop-yield forecasting models can increase overall model performance. Recent advances in remote sensing technologies provide an unprecedented resource for Earth observation that has both, spatial and temporal resolutions appropriate for precision agriculture applications. Furthermore, computationally efficient assimilation techniques can integrate these new satellite-derived products into modeling frameworks. To date, such modeling approaches work at the regional scale, with comparatively few studies examining the integration of remote sensing and crop-yield modeling at intra-field resolutions. In this study, we investigate the potential of assimilating daily, 3 m satellite-derived leaf area index (LAI) into the Agricultural Production Systems sIMulator (APSIM) for crop yield estimation in a rainfed corn field located in Nebraska. The impact of the number of satellite images and the definition of homogeneous spatial units required to re-initialize input parameters was also evaluated. Results show that the observed spatial variability of LAI within the maize field can effectively drive the crop simulation model and enhance yield forecasting that takes into account intra-field variability. The detection of intra-field biophysical metrics is particularly valuable since it may be employed to infer inefficiency problems at different stages of the season, and hence drive specific and localized management decisions for improving the final crop yield.</p>

scholarly journals Crop Yield Forecasting using Data Mining

2021 ◽  
Author(s):  
Pallavi Kamath ◽  
Pallavi Patil ◽  
Shrilatha S ◽  
Sushma ◽  
Sowmya S
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Yield Forecasting ◽  
Using Data ◽  
Crop Yield Forecasting

Empirical Analysis for Crop Yield Forecasting in India

2019 ◽  
Vol 9 (1) ◽  
pp. 132-138
Author(s):  
S. Dharmaraja ◽  
Vidyottama Jain ◽  
Priyanka Anjoy ◽  
Hukum Chandra
Keyword(s):  
Empirical Analysis ◽  
Crop Yield ◽  
Yield Forecasting ◽  
Crop Yield Forecasting
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