Ejemplares similares: Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model
- Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe
- The relative importance of rainfall, temperature and yield data for a regional scale crop model
- Increasing influence of heat stress on French maize yields from the 1960s to the 2030s
- Identifying traits for genotypic adaptation using crop models
- Estimating model prediction error: Should you treat predictions as fixed or random?
- Calibrated Models for Major Crops and Cropping System using Existing Datasets in Morocco and Uzbekistan
Autor: Watson, J.
- Draft principles and guidelines for integrating ecosystem-based approaches to adaptation in project and policy design: a discussion document
- Risk factors for diarrheagenic Escherichia coli infection in children aged 6-24 months in peri-urban community, Nairobi, Kenya
- Epidemiology of antimicrobial resistant diarrheagenic Escherichia coli pathotypes from children, livestock and food in Dagoretti South, Nairobi Kenya
- Projet de principes et lignes directrices intégrant des approches d'adaptations écosystémiques à des projets et à la conception de politiques: a discusión
- Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model
- The relative importance of rainfall, temperature and yield data for a regional scale crop model
Autor: Challinor, Andrew J.
- The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations
- Fixing Our Broken Food System: The Why and How
- Influences of increasing temperature on Indian wheat: quantifying limits to predictability
- Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production
- An integrated adaptation and mitigation framework for developing agricultural research: Synergies and Trade-Offs
- Multimodel ensembles improve predictions of crop–environment–management interactions