Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Sotirios V Archontoulis"'
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the
Externí odkaz:
https://doaj.org/article/d04f2b76f8b248cd9faf8b0a344c5624
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield predic
Externí odkaz:
https://doaj.org/article/d21839fd27114e5f93c9985a564de3d4
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 y
Externí odkaz:
https://doaj.org/article/8c3be05b37e54653af416d31c2667d44
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better pr
Externí odkaz:
https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b
Autor:
Elvis F. Elli, Ignacio A. Ciampitti, Michael J. Castellano, Larry C. Purcell, Seth Naeve, Patricio Grassini, Nicolas C. La Menza, Luiz Moro Rosso, André F. de Borja Reis, Péter Kovács, Sotirios V. Archontoulis
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this
Externí odkaz:
https://doaj.org/article/2c9724303f6049f6af63952dfb481787
Autor:
Caio L. dos Santos, Lori J. Abendroth, Jeffrey A. Coulter, Emerson D. Nafziger, Andy Suyker, Jianming Yu, Patrick S. Schnable, Sotirios V. Archontoulis
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1
Externí odkaz:
https://doaj.org/article/d39419ee504a4cbf95fbaed95bc16110
Autor:
German Mandrini, Sotirios V. Archontoulis, Cameron M. Pittelkow, Taro Mieno, Nicolas F. Martin
Publikováno v:
Data in Brief, Vol 40, Iss , Pp 107753- (2022)
Nitrogen (N) fertilizer recommendations for corn (Zea mays L.) in the US Midwest have been a puzzle for several decades, without agreement among stakeholders for which methodology is the best to balance environmental and economic outcomes. Part of th
Externí odkaz:
https://doaj.org/article/678b8885b59346fc87ab63c33d15fe88
Autor:
Ignacio A. Ciampitti, André Froes de Borja Reis, S. Carolina Córdova, Michael J. Castellano, Sotirios V. Archontoulis, Adrian A. Correndo, Luiz Felipe Antunes De Almeida, Luiz H. Moro Rosso
Publikováno v:
Frontiers in Plant Science, Vol 12 (2021)
Biological nitrogen (N) fixation is the most relevant process in soybeans (Glycine max L.) to satisfy plant N demand and sustain seed protein formation. Past studies describing N fixation for field-grown soybeans mainly focused on a single point time
Externí odkaz:
https://doaj.org/article/f9da3517e4964765b1fd51ec70e67b82
Publikováno v:
Frontiers in Plant Science, Vol 12 (2021)
We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and histor
Externí odkaz:
https://doaj.org/article/3fccf0d1568144478c709b03b4163d3b
Publikováno v:
Frontiers in Plant Science, Vol 11 (2020)
The emergence of new technologies to synthesize and analyze big data with high-performance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predic
Externí odkaz:
https://doaj.org/article/5e4ed90fef224b41b4b5b046553311a7