Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges

By James Hansen. 2002

414 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/dbd0119473b25ea76edc60426a8409aa3f25ec36

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology, Evolution, Behavior and Systematics

1. Andrew J. Challinor
2. K. Cassman
3. Daniel Rodríguez
4. V. Sadras
5. Tim Wheeler
6. J. Slingo
7. David B. Lobell
8. Francisco J. Doblas-Reyes

In Economics and Econometrics

1. Tim Wheeler
2. J. Slingo
3. Andrew J. Challinor
4. Francisco J. Doblas-Reyes
5. Holger Meinke
6. Roger Stone
7. Raymond K. Wong
8. Yehua Li

In Global and Planetary Change

1. Andrew J. Challinor
2. V. Sadras
3. Daniel Rodríguez
4. Tim Wheeler
5. J. Slingo
6. Francisco J. Doblas-Reyes
7. K. Cassman
8. Holger Meinke

Citations in the corpus

• Partially Linear Functional Additive Models for Multivariate Functional Data (2018, background): “It is well known that weather has a significant impact on crop yield, and statistical models can be used to relate weather forecast to crop yield prediction (Cadson, Todey, and Taylor 1996; Hansen 2002; Prasad et al. 2006; Lobell and Burke 2010).”
• Interactions between water and nitrogen in Australian cropping systems: physiological, agronomic, economic, breeding and modelling perspectives (2016, background): “The requirements for a forecast to be beneficial include (i) a climate-sensitive decision, (ii) a prediction that is skilful and timely, (iii) the ability to adjust the decision in light of the forecast, and (iv) the communication and support for the forecast (Hansen 2002).”
• Crop yield gaps: their importance, magnitudes, and causes. (2009, background): “importance of different types of uncertainties in farmer decision making and the ability of technologies to reduce each specific source of uncertainty (61).”
• Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research (2008, background): “Opportunities for the use of seasonal climate forecasts arise in situations in which there is a combination of climatic predictability, system response, and decision capacity (Hansen 2002).”
• Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles (2005, methodology): “on the principles outlined in this study could potentially be used as part of a hybrid (modelling plus descriptive) decision support system, such as that described by Hansen (2002). The use of the August forecast to update the crop model did not appear to have a significant impact on the results (Fi”
• Seasonal and Inter-Annual Climate Forecasting: The New Tool for Increasing Preparedness to Climate Variability and Change In Agricultural Planning And Operations (2005, background): “Australia, Southeast Asia, Africa, parts of theUS and SouthAmerica (Meinke et al., 2001; Gadgil et al., 2002; Hansen, 2002; Jones et al., 2000; Jagtap et al., 2002).”
• Constraints and opportunities in applying seasonal climate forecasts in agriculture (2007, background): “For forecasts to be of value to agriculture they must meet several criteria (Hansen 2002; Meinke and Stone 2005), which include:”