Search

The 4th Global Food Security conference highlighted four major developments: the shift from food security to food systems; a focus on diets and consumption patterns; the importance of unknown futures and inherent uncertainties and risks; and the central role of multi-level connections between local- and global-oriented research. These shifts highlight the importance for research to contribute to dialogue and collective intelligence through evidence-based brokerage, and to move beyond polarization of debates. These shifts also call for the involvement of scientists in multi-stakeholder arrangements to strengthen innovation and learning at different levels, and for their participation in foresight studies to help navigate plausible futures. Delegates discussed five scientific challenges to be addressed through both research investments and by improving science-policy interfaces. © 2021

The 4th Global Food Security conference highlighted four major developments: the shift from food security to food systems; a focus on diets and consumption patterns; the importance of unknown futures and inherent uncertainties and risks; and the central role of multi-level connections between local- and global-oriented research. These shifts highlight the importance for research to contribute to dialogue and collective intelligence through evidence-based brokerage, and to move beyond polarization of debates. These shifts also call for the involvement of scientists in multi-stakeholder arrangements to strengthen innovation and learning at different levels, and for their participation in foresight studies to help navigate plausible futures. Delegates discussed five scientific challenges to be addressed through both research investments and by improving science-policy interfaces. © 2021

Adequate tools for evaluating sustainable intensification (SI) of crop production for agro-hydrological system are not readily available. Building on existing concepts, we propose a framework for evaluating SI at the field and river basin levels. The framework serves as a means to assess and visualise SI indicator values, including yield, water-use efficiency and nitrogen-use efficiency (NUE), alongside water and nitrogen surpluses and their effects on water quantity and quality. To demonstrate the SI assessment framework, we used empirical data for both the field level (the Static Fertilization Experiment at Bad Lauchstädt) and the river basin level (the Selke basin, 463 km2) in central Germany. Crop yield and resource use efficiency varied considerably from 1980 to 2014, but without clear trends. NUE frequently fell below the desirable range (<50%), exposing the environment to a large N surplus (>80 kg N ha−1). For the catchment as a whole, the average nitrate-N concentration (3.6 mg L−1) was slightly higher than the threshold of 2.5 mg L−1 nitrate-N in surface water. However, weather and climate-related patterns, due to their effects on transport capacity and dilution, influenced water quantity and quality indicators more than agronomic practices. To achieve SI of crop production in the Selke basin, irrigation and soil moisture management are required to reduce yield variability and reduce N surpluses at field level. In addition, optimum application of fertiliser and manure could help to reduce the nitrate-N concentration below the set water quality standards in the Selke basin. In this way, there is scope for increase in yields and resource use efficiencies, and thus potential reduction of environmental impacts at basin level. We conclude that the framework is useful for assessing sustainable production, by simultaneously considering objectives related to crop production, resource-use efficiency and environmental quality, at both field and river basin levels.

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.

The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches., The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.

This data set consists of observations that were done during the PPS winter wheat field trials in Wageningen (The Netherlands). The experiment was conducted by the chair group Plant Production Systems (PPS) of Wageningen University and Research.The experiment consisted of two growing seasons (2013-2014 and 2014-2015), three nitrogen fertilization treatments (N1, N2, N3) and three cultivars (cv Julius, cv Ritmo, cv Tabasco).The experiment has been described in detail by Berghuijs et al (2023). The uploaded files are: 20231228_pps_winterwheat_trials_crop_nitrogen_and_phosphor.xlsx: contains measurements of crop N and crop P concentrations20231228_pps_winterwheat_trials_dry_matter.xlsx: contains measurements of dry matter, dry matter partitioning and specific leaf area20231228_pps_winterwheat_trials_phenology.xlsx: contains the sowing dates, dates of anthesis and harvest dates. Note that there were no separate measurements of phenology done per cultivar; only per growing season. References:Berghuijs, H. N. C., Silva, J. V., Rijk, H. C. A., van Ittersum, M. K., van Evert, F. K. & Reidsma, P. (2023). Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands. Field Crops Research 296: 108891. URL: https://doi.org/10.1016/j.fcr.2023.108891, This data set consists of observations that were done during the PPS winter wheat field trials in Wageningen (The Netherlands). The experiment was conducted by the chair group Plant Production Systems (PPS) of Wageningen University and Research.The experiment consisted of two growing seasons (2013-2014 and 2014-2015), three nitrogen fertilization treatments (N1, N2, N3) and three cultivars (cv Julius, cv Ritmo, cv Tabasco).The experiment has been described in detail by Berghuijs et al (2023). The uploaded files are: 20231228_pps_winterwheat_trials_crop_nitrogen_and_phosphor.xlsx: contains measurements of crop N and crop P concentrations20231228_pps_winterwheat_trials_dry_matter.xlsx: contains measurements of dry matter, dry matter partitioning and specific leaf area20231228_pps_winterwheat_trials_phenology.xlsx: contains the sowing dates, dates of anthesis and harvest dates. Note that there were no separate measurements of phenology done per cultivar; only per growing season. References:Berghuijs, H. N. C., Silva, J. V., Rijk, H. C. A., van Ittersum, M. K., van Evert, F. K. & Reidsma, P. (2023). Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands. Field Crops Research 296: 108891. URL: https://doi.org/10.1016/j.fcr.2023.108891

Despite its frequent use in policy discussions on future agricultural production, both the concept of the yield gap and its determinants are understood differently by economists and agronomists. This study provides a micro-level framework that disentangles and integrates agronomic and economic approaches to yield gap measurement. It decomposes the conventional yield gap indicator into four components that together provide a better understanding of why actual farm yield falls below potential: (1) the technical efficiency yield gap, (2) the allocative yield gap, (3) the economic yield gap and (4) the technology yield gap. The results can be used to inform targeted policy and farming recommendations at plot, farm household, local and national level. The framework is operationalised and tested by combining results from crop models with detailed farm and plot level survey data for maize production in Tanzania.