► Crop modelling frameworks (CMFs) have different levels of flexibility. ► Crop modellers should be at the interface with crop physiologist and software engineers. ► We need a better documentation of ...the crop modelling process. ► CMF can be used for integrated assessment study or further understanding of crop physiology.
Modular frameworks for crop modelling have evolved through simultaneous progress in crop science and software development but differences among these frameworks exist which are not well understood, resulting in potential misuse for crop modelling. In this paper we review differences and similarities among different developed frameworks and identify some implications for crop modelling. We consider three modelling frameworks currently used for crop modelling: CROSPAL (CROp Simulator: Picking and Assembling Libraries), APES (Agricultural Production and Externalities Simulator) and APSIM (Agricultural Production Systems sIMulator). The frameworks are implemented differently and they provide more or less flexibility and guidance, to facilitate assembly of crop model from model components. We underline the importance of systematic approaches to facilitate the selection of appropriate model structure and derive suggestions to facilitate it. We particularly stress the need for better documentation of the underlying assumptions of the modules on simulated processes and on the criteria applied in the selection of these modules for a particular simulation objective. Such documentation should help to point out the sources of uncertainties associated with the development of crop models and to reinforce the role of the crop modeller as an intermediary between the software engineer, coding the modules, and the end users, agronomists or crop physiologists using the model for a specific objective. Finally, the key contributions of modelling frameworks in the crop modelling domain are discussed and we draw conclusions for the prospects of such frameworks in the crop modelling field which should continue to reside on the principles of systems analysis but combined with up-to-date advances in software engineering techniques.
Climatic conditions and hence climate change influence agriculture. Most studies that addressed the vulnerability of agriculture to climate change have focused on potential impacts without ...considering adaptation. When adaptation strategies are considered, socio-economic conditions and farm management are often ignored, but these strongly influence current farm performance and are likely to also influence adaptation to future changes. This study analysed the adaptation of farmers and regions in the European Union to prevailing climatic conditions, climate change and climate variability in the last decades (1990–2003) in the context of other conditions and changes. We compared (1) responses in crop yields with responses in farmers’ income, (2) responses to spatial climate variability with responses to temporal climate variability, (3) farm level responses with regional level responses and (4) potential climate impacts (based on crop models) with actual climate impacts (based on farm accountancy data). Results indicated that impacts on crop yields cannot directly be translated to impacts on farmers’ income, as farmers adapt by changing crop rotations and inputs. Secondly, the impacts of climatic conditions on spatial variability in crop yields and farmers’ income, with generally lower yields in warmer climates, is different from the impacts of temporal variability in climate, for which more heterogeneous patterns are observed across regions in Europe. Thirdly, actual impacts of climate change and variability are largely dependent on farm characteristics (e.g. intensity, size, land use), which influence management and adaptation. To accurately understand impacts and adaptation, assessments should consider responses at different levels of organization. As different farm types adapt differently, a larger diversity in farm types reduces impacts of climate variability at regional level, but certain farm types may still be vulnerable. Lastly, we observed that management and adaptation can largely reduce the potential impacts of climate change and climate variability on crop yields and farmers’ income. We conclude that for reliable projections of the impacts of climate change on agriculture, adaptation should not be seen anymore as a last step in a vulnerability assessment, but as integrated part of the models used to simulate crop yields, farmers’ income and other indicators related to agricultural performance.
Crop growth models are used for a wide range of objectives. For each objective a specific model has to be developed, because the reusability of a model is often limited by the necessity of a ...fundamental restructuring to adapt it to a different objective. To overcome this limitation, we developed a method to facilitate model restructuring by a novel combination of software technology with expert knowledge.
This resulted in the decision-making software application CROSPAL (CROp Simulator: Picking and Assembling Libraries). CROSPAL includes (1) a library of processes each containing different modelling approaches for each crop physiological process and (2) a procedure based on expert knowledge of how to combine the different processes for the objective of the simulation.
A brief overview of the state of the art in crop modelling is presented, followed by an account of the developed concept to improve flexibility in crop modelling considering expert knowledge. We describe the design of the software and how expert knowledge is integrated. The use of CROSPAL is illustrated for the modelling of crop phenology. We conclude that CROSPAL is a helpful tool to improve flexibility in crop modelling considering expert knowledge but further development and evaluation is required to extend its range of application to more processes and issues crop modelling is presently addressing.
Assessments of the relationships between crop productivity and climate change rely upon a combination of modelling and measurement. As part of this review, this relationship is discussed in the ...context of crop and climate simulation. Methods for linking these two types of models are reviewed, with a primary focus on large-area crop modelling techniques. Recent progress in simulating the impacts of climate change on crops is presented, and the application of these methods to the exploration of adaptation options is discussed. Specific advances include ensemble simulations and improved understanding of biophysical processes. Finally, the challenges associated with impacts and adaptation research are discussed. It is argued that the generation of knowledge for policy and adaptation should be based not only on syntheses of published studies, but also on a more synergistic and holistic research framework that includes: (i) reliable quantification of uncertainty; (ii) techniques for combining diverse modelling approaches and observations that focus on fundamental processes; and (iii) judicious choice and calibration of models, including simulation at appropriate levels of complexity that accounts for the principal drivers of crop productivity, which may well include both biophysical and socio-economic factors. It is argued that such a framework will lead to reliable methods for linking simulation to real-world adaptation options, thus making practical use of the huge global effort to understand and predict climate change.
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ...ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual ...models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment.
Food production must adapt in the face of climate change. In Europe, projected vulnerability of food production to climate change is particularly high in Mediterranean regions. Increasing ...agricultural diversity has been suggested as an adaptation strategy, but empirical evidence is lacking. We analyzed the relationship between regional farm diversity (i.e., diversity among farm types) and the effects of climate variability on regional wheat (Triticumspp.) productivity. An extensive data set with information from more than 50 000 farms from 1990 to 2003 was analyzed, along with observed weather data. Our results suggest that the diversity in farm size and intensity, particularly high in Mediterranean regions, reduces vulnerability of regional wheat yields to climate variability. Accordingly, increasing regional farm diversity can be a strategy through which regions in Europe can adapt to unfavorable conditions, such as higher temperatures and associated droughts.
Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a ...32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.
Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1% points, representing a relative change of −8.6%.
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•Eddy covariance measurements were used to inter-compare 29 maize models in their ability to simulate evapotranspiration (ET).•There was a huge range among the models in their ...simulations of ET for the initial blind phase, which continued even as more information was supplied.•Medians of the 29 models were generally close to observations.•Both simple and complex models were among the best in their ability to simulate ET.•Widely used models were consistently among the best, presumably because large numbers of users with a wide range of conditions led to improvements.
Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first “blind” phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET.
The effects of aggregating soil data (DAE) by areal majority of soil mapping units was explored for regional simulations with the soil-vegetation model CoupModel for a region in Germany (North ...Rhine-Westphalia). DAE were analysed for wheat yield, drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE were higher for soil C mineralization and N leaching than for yield and drainage and were strongly related to the presence of specific soils within the study region. These soil types were associated to extreme simulated output variables compared to the mean variable in the region. The spatial aggregation of these key functional soils within sub-regions additionally influenced the DAE. A spatial analysis of their spatial pattern (i.e. their presence/absence, coverage and aggregation) can help in defining the appropriate grid resolution that would minimize the error caused by aggregating soil input data in regional simulations.
•Soil variability explains data aggregation effects (DAE) in regional modelling.•We quantified DAE on simulated yield, drainage, C mineralisation and N leaching.•We investigated the relative importance of different soils to DAE.•Key soils generating extreme values were identified within the region.•The spatial distribution of key soils should be evaluated when upscaling soil data.
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