1,721,188 research outputs found
How to assess climate change impacts on farmers' crop yields?
No full textFarmers' yields are affected by multiple environmental and socioeconomic factors. Crop simulation models that are thoroughly calibrated and evaluated for local conditions and fed with data from climate change projections are principally well-suited to estimate the impacts of climate change on potential yields, assuming optimal management. Important question is, however, what will happen to the yields on farmers fields in the future and to the gap between actual and potential yields. This will require linking crop model-based impact projections with socioeconomic analysis. In Finland, farmer's crop yields have been steadily increasing after World War II, mainly due to improvements in agro-management driven by technological development and genetic improvements with higher-yielding new cultivars. During past few decades, however, the yield gap has increased as farmers put less emphasis on high crop yields but apply cost-reducing management. This is mainly due to discouraging input and output prices and subsidy systems. Comprehensive yield series (1971 to present) from Finnish experimental and farmers' fields provide the basis to analyse yield trends, yield-influencing factors and develop modelling tools for improved prediction of future actual yields under climate change. Crop simulation model WOFOST was used to simulate historical (1971-2008) and future (2011-2040, 2041-2070) potential yields of spring barley, in two regions representing different agro-ecological zones in Finland. The development of historical yield gaps was analysed and linked to the information on socioeconomic developments. This is to contribute to the discussion on uncertainties related to climate change impact projections taking into account both environmental and socioeconomic drivers. In conclusion, more integrated efforts are needed to develop modelling tools taking into account both, environmental and socioeconomic effects on farmer's behavior and future yields. Most measures to narrow current yield gaps also have a high potential to maintain or increase crop yield levels under future climatic conditions
How to assess climate change impacts on farmers' crop yields?
No full textFarmers' yields are affected by multiple environmental and socioeconomic factors. Crop simulation models that are thoroughly calibrated and evaluated for local conditions and fed with data from climate change projections are principally well-suited to estimate the impacts of climate change on potential yields, assuming optimal management. Important question is, however, what will happen to the yields on farmers fields in the future and to the gap between actual and potential yields. This will require linking crop model-based impact projections with socioeconomic analysis. In Finland, farmer's crop yields have been steadily increasing after World War II, mainly due to improvements in agro-management driven by technological development and genetic improvements with higher-yielding new cultivars. During past few decades, however, the yield gap has increased as farmers put less emphasis on high crop yields but apply cost-reducing management. This is mainly due to discouraging input and output prices and subsidy systems. Comprehensive yield series (1971 to present) from Finnish experimental and farmers' fields provide the basis to analyse yield trends, yield-influencing factors and develop modelling tools for improved prediction of future actual yields under climate change. Crop simulation model WOFOST was used to simulate historical (1971-2008) and future (2011-2040, 2041-2070) potential yields of spring barley, in two regions representing different agro-ecological zones in Finland. The development of historical yield gaps was analysed and linked to the information on socioeconomic developments. This is to contribute to the discussion on uncertainties related to climate change impact projections taking into account both environmental and socioeconomic drivers. In conclusion, more integrated efforts are needed to develop modelling tools taking into account both, environmental and socioeconomic effects on farmer's behavior and future yields. Most measures to narrow current yield gaps also have a high potential to maintain or increase crop yield levels under future climatic conditions
A Modelling Framework for Assessing Adaptive Management Options of Finnish Agrifood Systems to Climate Change
No full textImproved assessment methods for agriculture production systems are needed to identify the risks and opportunities related to global changes in climate, markets and policies, and the consequences of alternative options of coping with and mitigating the changes. This paper presents the AGRISIMU modelling framework developed for ex-ante assessment of alternative policy and management options meant to support farms and agrifood sector adapt to climate change, maintain biodiversity and reduce nutrient emissions under Finnish conditions. The modelling framework represents a novel approach to the integration of data and output from several existing models like a dynamic regional sector model of Finnish agriculture, a farm-level optimisation model, a dynamic crop growth simulation model and models describing the nutrient dynamics in agricultural systems and a hydrological rainfall-runoff model. The framework is particularly aimed for Nordic conditions and to serve as an assessment tool that considers multiple factor and scale interactions
Sustainable intensification of crop production under alternative future changes in climate and technology: The case of the North Savo region
No full text202
Effects of changes in climatic means, variability, and agro-technologies on future wheat and maize yields at 10 sites across the globe
No full textProposal and extensive test of a calibration protocol for crop phenology models
No full textAbstract A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Academy of Finland http://dx.doi.org/10.13039/501100002341Rheinische Friedrich-Wilhelms-Universität Bonn http://dx.doi.org/10.13039/50110000813
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