121 research outputs found
The Science-for-Life Partnerships: Does size really matter, and can ICT help?
No full textThis study introduces findings of an initial pilot from a New Zealand government-funded initiative known as Science-for-Life, which aims to enhance the quality of science teaching through the formation of face-to-face and virtual learning partnerships involving crown research institutes (CRIs) and primary and secondary schools. Using a case study methodology, it describes and analyses a trial partnership between the CRI, Scion Research, and teachers of Seadown Primary School in Hamilton. The study uses Grobe's (1990) typology of industry-education partnerships as an analytical "lens" through which to evaluate the characteristics of the partnership, and explores the role that ICT played in establishing and sustaining it, well beyond its anticipated conclusion. Findings indicate that while in terms of Grobe's framework a genuine partnership label may not have been appropriate in this case, the interaction nonetheless proved to be extremely valuable in supporting learning goals, and that while ICT played a significant role in this, it was not fundamental to the partnership's success
Twelve years of iPads and apps in schools : what conditions support effective practices in K-6 classrooms?
No full textSince their release in 2010, iPads and their associated apps have been touted as ‘game changers’ for schools struggling with technology provisioning issues, that limited their ability to fully leverage the educational potential of digital devices on a ‘whole class’ basis. Since then, a variety of schemes have been implemented such as ‘Bring Your Own Device’ (BYOD) and portable ‘device pods’, as systems for improving access to, and utilisation of, mobile technologies in classroom curriculum. In many schools, concurrent to these initiatives have been improvements in technology infrastructure, including upgrades to external connectivity via the advent of high-speed fibre-based broadband, and internally through the establishment of school wifi networks and associated online security systems. Aligned with these developments has been a growing body of research exploring how teachers at all levels of education systems have incorporated these new resources into their curriculum, and examining what, if any, benefits have resulted. This article is an analysis of key findings from four published studies undertaken by the author between 2015 and 2021 in New Zealand K-6 schools, to build understanding of factors that contributed to the effective practices with mobile devices witnessed in the research classrooms. While numerous separate studies have been undertaken exploring specific outcomes from the use of iPads and other mobile technologies in different educational contexts, the analysis presented in this article attempts to identify common factors existing across four purposively selected studies, that contributed to their success. The studies were deliberately chosen to provide a broad overview of applications of this technology in different K-6 classrooms for different purposes, supporting deeper understanding of the factors that underpin effective teaching and learning with and through mobile devices, in schools. This is important, as it builds knowledge of the fundamental foundations to effective educational use of mobile devices, regardless of the learning context in which they are used, and could assist teachers in designing, implementing and assessing curricular that optimises the learning potential of these devices. Copyright © 2023 Falloon
Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation
No full textAbstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are neededAbstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are neededAbstract
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are neededNational Science Board https://doi.org/10.13039/10000571
Climate Change Impacts on Global Agriculture
No full textBased on predicted changes in the magnitude and distribution of global precipitation, temperature and river flow under the IPCC SRES A1B and A2 scenarios, this study assesses the potential impacts of climate change and CO2 fertilization on global agriculture. The analysis uses the new version of the GTAP-W model, which distinguishes between rainfed and irrigated agriculture and implements water as an explicit factor of production for irrigated agriculture. Future climate change is likely to modify regional water endowments and soil moisture. As a consequence, the distribution of harvested land would change, modifying production and international trade patterns. The results suggest that a partial analysis of the main factors through which climate change will affect agricultural productivity lead to different outcomes. Our results show that global food production, welfare and GDP fall in the two time periods and SRES scenarios. Higher food prices are expected. Independently of the SRES scenario, expected losses in welfare are marked in the long term. They are larger under the SRES A2 scenario for the 2020s and under the SRES A1B scenario for the 2050s. The results show that countries are not only influenced by regional climate change, but also by climate-induced changes in competitiveness.Computable General Equilibrium, Climate Change, Agriculture, Water Resources, River Flow
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES maize simulations
No full textThis data set contains output data from simulations with the model JULES for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season')
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES rice simulations
No full textThis data set contains output data from simulations with the model JULES for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season')
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES soybean simulations
No full textThis data set contains output data from simulations with the model JULES for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season')
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