► Linear interpolation can generate daily weather data from aggregated weather data.
► Simulations using interpolated data will deviate from those with actual data.
► Increasing day-to-day ...variability in weather conditions gives stronger deviations.
► Increasing detail in a model structure results in stronger deviations.
Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather–crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages.
Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data.
Integrated Assessment and Modelling (IAM) provides an interdisciplinary approach to support ex-ante decision-making by combining quantitative models representing different systems and scales into a ...framework for integrated assessment. Scenarios in IAM are developed in the interaction between scientists and stakeholders to explore possible pathways of future development. As IAM typically combines models from different disciplines, there is a clear need for a consistent definition and implementation of scenarios across models, policy problems and scales. This paper presents such a unified conceptualization for scenario and assessment projects. We demonstrate the use of common ontologies in building this unified conceptualization, e.g. a common ontology on assessment projects and scenarios. The common ontology and the process of ontology engineering are used in a case study, which refers to the development of SEAMLESS-IF, an integrated modelling framework to assess agricultural and environmental policy options as to their contribution to sustainable development. The presented common ontology on assessment projects and scenarios can be reused by IAM consortia and if required, adapted by using the process of ontology engineering as proposed in this paper.
Effects of increasing carbon dioxide concentration CO2 on wheat vary depending on water supply and climatic conditions, which are difficult to estimate. Crop simulation models are often used to ...predict the impact of global atmospheric changes on food production. However, models have rarely been tested for effects on crops of CO2 and drought for different climatic conditions due to limited data available from field experiments. Simulations of the effects of elevated CO2 and drought on spring wheat (Triticum aestivum L.) from three crop simulation models (LINTULCC2, AFRCWHEAT2, Sirius), which differ in structure and mechanistic detail, were compared with observations. These were from 2 years of free-air carbon dioxide enrichment (FACE) experiments in Maricopa, Arizona and 2 years of standardised (in crop management and soil conditions) open-top chamber (OTC) experiments in Braunschweig and Giessen, Germany. In a simulation exercise, models were used to assess the possible impact of increased CO2 on wheat yields measured between 1987 and 1999 at one farm site in the drought prone region of Andalucia, south Spain. The models simulated well final biomass (BM), grain yield (GY), cumulative evapotranspiration (ET) and water use efficiency (WUE) of wheat grown in the FACE experiments but simulations were unsatisfactory for OTC experiments. Radiation use efficiency (RUE) and yield responses to CO2 and drought were on average higher in OTC than in FACE experiments. However, there was large variation among OTC experiments. Plant growth in OTCs was probably modified by several factors related to plot size, the use (or not use) of border plants, airflow pattern, modification of radiation balance and/or restriction of rooting volume that were not included in the models. Variation in farm yields in south Spain was partly explained by the models, but sources of unexplained yield variation could not be identified and were most likely related to effects of pests and diseases that were not included in the models. Simulated GY in south Spain increased in the range between 30 and 65 ue to doubling CO2. The simulated increase was larger when a CO2xdrought interaction was assumed (LINTULCC2, AFRCWHEAT2) than when it was not (Sirius). It was concluded that crop simulation models are able to reproduce wheat growth and yield for different CO2 and drought treatments in a field environment. However, there is still uncertainty about the combined effects of CO2 and drought including the timing of drought stress and about relationships that determine yield variation at farm and larger scales that require further investigation including model testing.
► A global spatial assessment of crop heat-stress for wheat, maize, rice and soybean. ► Hot-spots of heat-stress were found mostly in continental lands at high latitude. ► Risk of crop damage mostly ...increased for future climate change scenario. ► Adaptation of agricultural technologies is necessary to reduce risk of heat-stress.
The productivity of important agricultural crops is drastically reduced when they experience short episodes of high temperatures during the reproductive period. Crop heat stress was acknowledged in the IPCC 4th Assessment Report as an important threat to global food supply. We produce a first spatial assessment of heat stress risk at a global level for four key crops, wheat, maize, rice and soybean, using the FAO/IIASA Global Agro-Ecological Zones Model (GAEZ). A high risk of yield damage was found for continental lands at high latitudes, particularly in the Northern Hemisphere between 40 and 60°N. Central and Eastern Asia, Central North America and the Northern part of the Indian subcontinent have large suitable cropping areas under heat stress risk. Globally, this ranged from less than 5Mha of suitable lands for maize for the baseline climate (1971–2000) to more than 120Mha for wetland rice for a future climate change condition (2071–2100) assuming the A1B emission scenario. For most crops and regions, the intensity, frequency and relative damage due to heat stress increased from the baseline to the A1B scenario. However for wheat and rice crops, GAEZ selection of different crop types and sowing dates in response to A1B seasonal climate caused a reduction in heat stress impacts in some regions, which suggests that adaptive measures considering these management options may partially mitigate heat stress at local level. Our results indicate that temperate and sub-tropical agricultural areas might bear substantial crop yield losses due to extreme temperature episodes and they highlight the need to develop adaptation strategies and agricultural policies able to mitigate heat stress impacts on global food supply.
► Plant growth models inadequately account for farm management. ► Five approaches of modelling farm management are considered. ► Their effects on simulated harvest dates and yields are compared. ► ...The approaches significantly differ in the accuracy of simulated harvest dates. ► Accuracy of simulated yields is sensitive to a different climate/harvest timing.
Despite their wide range of applications, process-based plant (crop and grassland) growth models often fail to reproduce yields, particularly at farm, regional and larger scales. This is largely due to inadequate information about field management activities needed as input to these models. A promising approach to overcome this limitation is to link plant growth models with farm management models which allow the simulation of management activities considering farmers’ aims and constraints. Different approaches to model farm management are available, but tangible results to justify the choice for a specific approach are lacking. The objective of this work was to compare the effects of different approaches of modelling farm management on the simulation of grassland mechanized harvest dates and yields. Simulations were run with each approach for two grassland-based beef farms and 3years and compared with available data over 156 harvest events. Our results show significant differences in the accuracy of simulated harvest dates depending on the approach to model farm management. Approaches using fixed dates or optimal phenological stages determined by expert knowledge performed less accurate than the one using calibrated phenological stages. Best results were achieved with a detailed farm management model. The accuracy of simulated yields was less affected by the chosen farm management modelling approach. However, this differed depending on the climate and the timing of harvest, allowing to rank approaches according to their ability to simulate harvest dates and yields. We conclude that further investigation is required to generalize these findings to other farm types including arable farming, and to support the analysis, modelling and calibration of farmers’ management decision processes.
► A protocol is needed to guide modular framework’s users in the selection of modules. ► The protocol reports on the decisions made during the modelling process. ► The use of uncertainty matrix ...prevents from misusing a modular crop modelling framework. ► Model building should be seen as an iterative process.
Crop models require different structures for different applications. Modular and flexible crop modelling frameworks, such as the recently developed agricultural production and externalities simulator (APES), support the change of model structure. However, the assembly of different modules to create a model may not always result in the best model structure. We developed and tested a protocol for a systematic selection and evaluation of a crop growth model structure. The novelty of the presented protocol relies on a throughout analysis of the different modelling approaches (modules) and on how to assemble them to create new modelling solutions (i.e. model). We use a case study to demonstrate that we can explicitly express and test the different assumptions behind the choice of a specific modelling approach. Our case study refers to the simulation of crop growth in response to nitrogen management and the importance of an accurate simulation of the nitrogen uptake. Applying the proposed protocol, we identify the need to improve the initially selected nitrogen mineralisation module. We conclude that not only is the protocol suitable to provide guidance for systematic testing of different crop processes modelled, but also its use highlights the importance of the documentation of the modelling process and of the clarification of the uncertainty associated with the model structure.
Background and Aims: The problem of increasing CO2 concentration CO2 and associated climate change has generated much interest in modelling effects of CO2 on plants. While variation in growth and ...productivity is closely related to the amount of intercepted radiation, largely determined by leaf area index (LAI), effects of elevated CO2 on growth are primarily via stimulation of leaf photosynthesis. Variability in LAI depends on climatic and growing conditions including CO2 concentration and can be high, as is known for agricultural crops which are specifically emphasized in this report. However, modelling photosynthesis has received much attention and photosynthesis is often represented inadequately detailed in plant productivity models. Less emphasis has been placed on the modelling of leaf area dynamics, and relationships between plant growth, elevated CO2 and LAI are not well understood. This Botanical Briefing aims at clarifying the relative importance of LAI for canopy assimilation and growth in biomass under conditions of rising CO2 and discusses related implications for process-based modelling. Model: A simulation exercise performed for a wheat crop demonstrates recent experimental findings about canopy assimilation as affected by LAI and elevation of CO2. While canopy assimilation largely increases with LAI below canopy light saturation, effects on canopy assimilation of CO2 elevation are less pronounced and tend to decline as LAI increases. Results from selected model-testing studies indicate that simulation of LAI is often critical and forms an important source of uncertainty in plant productivity models, particularly under conditions of limited resource supply. Conclusions: Progress in estimating plant growth and productivity under rising CO2 is unlikely to be achieved without improving the modelling of LAI. This will depend on better understanding of the processes of substrate allocation, leaf area development and senescence, and the role of LAI in controlling plant adaptation to environmental changes.
Long-term future development of European agriculture within the global market is highly uncertain, but can potentially have large impacts on the future of agricultural businesses, rural communities ...and amenities such as traditional landscapes and biodiversity. Despite great uncertainties it is of interest to explore the extent of these potential changes. This paper provides an explorative scenario of the European crop production in a liberalised world without European Union (EU) market interventions. The results do not form a prediction or a business as usual scenario, but rather a plausible and salient thought-experiment of a possible future based on the consistent integration of current conceptual and quantitative models.
Future scenarios for climate, demography, technology and global demand for agricultural commodities are used to assess the competitiveness of European agriculture. Regional economic competitiveness is determined by combining indicators for the economic strength of farms in a region and population pressure on agricultural land, and subsequently used to determine where agricultural production is likely to sustain under the market liberalisation scenario. The method is illustrated for the 27 EU member state countries for three commodities: wheat, potato and milk (relying on grass).
Results include maps of the dominant wheat, potato and milk producing regions across Europe as projected for 2050. They show that due to increased agricultural productivity, less agricultural land will be needed to supply the European demand for food and feed. In addition, production will concentrate in those regions which have a comparative advantage. This potentially leads to a strong polarisation between north-western Europe and southern Europe, which faces negative impacts of climate change and central and northern Europe where agricultural businesses lag in economic strength and farm size. A contrasting policy intervention scenario illustrates how differences in demand and productivity result in an expansion of the agricultural area, especially for the production of wheat.
Although the complete liberalisation scenario may seem unlikely, and the underlying assumptions have great uncertainty, the results help identify and map market pressures on agricultural land use across regions in Europe. As such, it stimulates policy debate on the desired future for the European agricultural sector and the trade-offs between economic competitiveness under global market conditions and policy intervention. In addition, it provides a basis for the planning of alternative economic strategies for agriculturally less competitive regions.
Scenario-based approaches in environmental and policy assessment studies are increasingly applied within integrated assessment and modelling frameworks. The SEAMLESS project develops such an ...integrated framework (SEAMLESS-IF) aiming to assess, ex-ante, impacts of alternative agro-environmental policies on the sustainability of agricultural systems. A particular challenge in this context is the consistent translation of a wide range of policy questions into scenarios that a modelling framework can assess. The present work defines a methodology for scenario-development in integrated policy assessment with specific emphasis on SEAMLESS-IF. After a general overview on scenario concepts for integrated policy assessment the adopted scenario concept and its development procedure is presented. They allow building integrated scenarios capturing the range of drivers of the assessed agricultural system in a consistent way across temporal and spatial scales. Then focus is on the particular procedures to translate the policy assessment questions into scenario parameters and to implement these parameters into SEAMLESS-IF. Two examples targeted at European and regional level combining integrated assessments of policy changes and technological innovations are considered to illustrate the SEAMLESS scenario concept. We conclude that the proposed methodology to translate policy assessment problems into scenarios effectively supports integrated assessment in SEAMLESS-IF or even in other modelling frameworks.
► The process of model building using modular frameworks asks for interdisciplinary work. ► Use of the systematic approach for model re-assembling promotes transparent and reproducible models. ► Crop ...modelling framework facilitates models’ changes at three levels: parameters, equations, and structure. ► Use of the approach helps to control model complexity while simulating emergent properties at crop level.
The process of crop modelling to develop operational software requires different skills, from conceptualization of the biophysical system to computer programming, involving three main scientific disciplines: agronomy, mathematics, and software engineering. Model building implies transforming a conceptual model into sets of mathematical equations and then translating these equations into a computer program. Although recent crop modelling frameworks can technically support model building, the modelling process is not always well documented and difficult to repeat. The focus of this paper is therefore on developing and documenting an approach to re-assemble crop models, i.e. develop a new model from an existing one, using a crop modelling framework and crop physiological knowledge. Modifications to an initial crop model were classified according to three categories: (i) changes in parameter values, (ii) changes in equations, and (iii) changes in overall model structure. We illustrate the approach with a case study transforming a wheat crop model into a pea crop model. We discuss the role of each actor in the process to document diverse uncertainties related to the model (i.e. contextual situation, data, structure), and the general applicability of the approach for different crop modelling frameworks. We conclude that the use of our approach to re-assemble a crop model within a modelling framework facilitates integration of different disciplines around a modelling objective, and facilitates creating transparent and reproducible models.
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