Effect of Crop Phenology and Habitat on the Diet of Common Grackles (Quiscalus quiscula)

1994 ◽  
Vol 131 (2) ◽  
pp. 381 ◽  
Author(s):  
H. Jeffrey Homan ◽  
George M. Linz ◽  
William J. Bleier
Keyword(s):  
Crop Phenology ◽  
Quiscalus Quiscula

Common Grackle (Quiscalus quiscula)

1997 ◽  
Author(s):  
Brian D. Peer ◽  
Eric K. Bollinger
Keyword(s):  
Quiscalus Quiscula

Impact of Transgenic Bacillus thuringiensis Corn and Crop Phenology on Five Nontarget Arthropods

2005 ◽  
Vol 34 (5) ◽  
pp. 1302-1316 ◽  
Author(s):  
Clinton D. Pilcher ◽  
Marlin E. Rice ◽  
John J. Obrycki
Keyword(s):  
Bacillus Thuringiensis ◽  
Crop Phenology

Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

2021 ◽  
Author(s):  
Mehdi H. Afshar ◽  
Timothy Foster ◽  
Thomas P. Higginbottom ◽  
Ben Parkes ◽  
Koen Hufkens ◽  
...  
Keyword(s):  
Smallholder Farmers ◽  
Simulation Models ◽  
Extreme Weather ◽  
Index Insurance ◽  
Yield Estimation ◽  
Crop Phenology ◽  
Yield Data ◽  
Basis Risk ◽  
Crop Simulation ◽  
Crop Simulation Models

<p>Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers. </p><p>In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets – a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insurance</p><p>We apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.</p>

Early Classification Method for US Corn and Soybean by Incorporating MODIS-Estimated Phenological Data and Historical Classification Maps in Random-Forest Regression Algorithm

2021 ◽  
Vol 87 (10) ◽  
pp. 747-758
Author(s):  
Toshihiro Sakamoto
Keyword(s):  
Random Forest ◽  
Classification Accuracy ◽  
Classification Method ◽  
Estimation Accuracy ◽  
Random Forest Regression ◽  
Crop Phenology ◽  
Phenological Data ◽  
Mixed Pixel ◽  
Crop Classification ◽  
Emergence Date

An early crop classification method is functionally required in a near-real-time crop-yield prediction system, especially for upland crops. This study proposes methods to estimate the mixed-pixel ratio of corn, soybean, and other classes within a low-resolution MODIS pixel by coupling MODIS-derived crop phenology information and the past Cropland Data Layer in a random-forest regression algorithm. Verification of the classification accuracy was conducted for the Midwestern United States. The following conclusions are drawn: The use of the random-forest algorithm is effective in estimating the mixed-pixel ratio, which leads to stable classification accuracy; the fusion of historical data and MODIS-derived crop phenology information provides much better crop classification accuracy than when these are used individually; and the input of a longer MODIS data period can improve classification accuracy, especially after day of year 279, because of improved estimation accuracy for the soybean emergence date.

Cotton crop phenology in a new temperature regime

2014 ◽  
Vol 285 ◽  
pp. 22-29 ◽  
Author(s):  
Qunying Luo ◽  
Michael Bange ◽  
Loretta Clancy
Keyword(s):  
Temperature Regime ◽  
Crop Phenology ◽  
Cotton Crop
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