Modelling food security : Bridging the gap between the micro and the macro scale
Müller, Birgit ; Hoffmann, Falk ; Heckelei, Thomas ; Müller, Christoph ; Hertel, Thomas W. ; Polhill, J.G. ; Wijk, Mark van; Achterbosch, Thom ; Alexander, Peter ; Brown, Calum ; Kreuer, David ; Ewert, Frank ; Ge, Jiaqi ; Millington, James D.A. ; Seppelt, Ralf ; Verburg, Peter H. ; Webber, Heidi - \ 2020
Global environmental change : human and policy dimensions 63 (2020). - ISSN 0959-3780
Agent-based models - Crop models - Economic equilibrium models - Food security - Land use - Model integration - Multi-scale interactions - Social-ecological feedbacks
Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours. We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement. We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.
GreenLight – An open source model for greenhouses with supplemental lighting : Evaluation of heat requirements under LED and HPS lamps
Katzin, David ; Mourik, Simon van; Kempkes, Frank ; Henten, Eldert J. van - \ 2020
Biosystems Engineering 194 (2020). - ISSN 1537-5110 - p. 61 - 81.
Crop models - Energy use - Greenhouse lighting - Greenhouse models - LEDs - Open source
Greenhouse models are important tools for the analysis and design of greenhouse systems and for offering decision support to growers. While many models are available, relatively few include the influence of supplementary lighting on the greenhouse climate and crop. This study presents GreenLight, a model for greenhouses with supplemental lighting. GreenLight extends state of the art models by describing the qualitative difference between the common lighting system of high-pressure sodium (HPS) lamps, and the newest technology for horticultural lighting - the light-emitting diodes (LEDs). LEDs differ from HPS lamps in that they operate at lower temperatures, emit mostly convective heat and relatively little radiative heat, and can be more efficient in converting electricity to photosynthetically active radiation (PAR). These differences can have major implications on the greenhouse climate and operation, and on the amount of heat that must be supplied from the greenhouse heating system. Model predictions have been evaluated against data collected in greenhouse compartments equipped with HPS and LED lamps. The model predicted the greenhouse's heating needs with an error of 8–51 W m−2, representing 1–12% of the measured values; the RMSE for indoor temperature was 1.74–2.04 °C; and the RMSE for relative humidity was 5.52–8.5%. The model is freely available as open source MATLAB software at https://github.com/davkat1/GreenLight. It is hoped that it may be further evaluated and used by researchers worldwide to analyse the influence of the most recent lighting technologies on greenhouse climate control.
Current knowledge and future research opportunities for modeling annual crop mixtures. A review
Gaudio, Noémie ; Escobar-Gutiérrez, Abraham J. ; Casadebaig, Pierre ; Evers, Jochem B. ; Gérard, Frédéric ; Louarn, Gaëtan ; Colbach, Nathalie ; Munz, Sebastian ; Launay, Marie ; Marrou, Hélène ; Barillot, Romain ; Hinsinger, Philippe ; Bergez, Jacques Eric ; Combes, Didier ; Durand, Jean Louis ; Frak, Ela ; Pagès, Loïc ; Pradal, Christophe ; Saint-Jean, Sébastien ; Werf, Wopke van der; Justes, Eric - \ 2019
Agronomy for Sustainable Development 39 (2019)2. - ISSN 1774-0746
Annual crop mixtures - Crop models - Functional–structural plant models - Genotypes mixtures - Individual-based models - Intercrops - Model users
Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand, and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that (1) the crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). (2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. Crop models are mainly used to address issues related to both crop mixtures management and the integration of crop mixtures into larger scales such as the rotation. In contrast, individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches.
Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts
Ogutu, Geoffrey E.O. ; Franssen, Wietse H.P. ; Supit, Iwan ; Omondi, P. ; Hutjes, Ronald W.A. - \ 2018
Agricultural and Forest Meteorology 250-251 (2018). - ISSN 0168-1923 - p. 243 - 261.
Crop models - Dynamic crop forecasting - East Africa - Forecast lead-time - Probabilistic ensemble prediction - Rainfed agriculture
We tested the usefulness of seasonal climate predictions for impacts prediction in eastern Africa. In regions where these seasonal predictions showed skill we tested if the skill also translated into maize yield forecasting skills. Using European Centre for Medium-Range Weather Forecasts (ECMWF) system-4 ensemble seasonal climate hindcasts for the period 1981–2010 at different initialization dates before sowing, we generated a 15-member ensemble of yield predictions using the World Food Studies (WOFOST) crop model implemented for water-limited maize production and single season simulation. Maize yield predictions are validated against reference yield simulations using the WATCH Forcing Data ERA-Interim (WFDEI), focussing on the dominant sowing dates in the northern region (July), equatorial region (March-April) and in the southern region (December). These reference yields show good anomaly correlations compared to the official FAO and national reported statistics, but the average reference yield values are lower than those reported in Kenya and Ethiopia, but slightly higher in Tanzania. We use the ensemble mean, interannual variability, mean errors, Ranked Probability Skill Score (RPSS) and Relative Operating Curve skill Score (ROCSS) to assess regions of useful probabilistic prediction. Annual yield anomalies are predictable 2-months before sowing in most of the regions. Difference in interannual variability between the reference and predicted yields range from ±40%, but higher interannual variability in predicted yield dominates. Anomaly correlations between the reference and predicted yields are largely positive and range from +0.3 to +0.6. The ROCSS illustrate good pre-season probabilistic prediction of above-normal and below-normal yields with at least 2-months lead time. From the sample sowing dates considered, we concluded that, there is potential to use dynamical seasonal climate forecasts with a process based crop simulation model WOFOST to predict anomalous water-limited maize yields.
Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science
Jones, James W. ; Antle, John M. ; Basso, Bruno ; Boote, Kenneth J. ; Conant, Richard T. ; Foster, Ian ; Godfray, H.C.J. ; Herrero, Mario ; Howitt, Richard E. ; Janssen, Sander ; Keating, Brian A. ; Munoz-Carpena, Rafael ; Porter, Cheryl H. ; Rosenzweig, Cynthia ; Wheeler, Tim R. - \ 2017
Agricultural Systems 155 (2017). - ISSN 0308-521X - p. 269 - 288.
Agricultural data - Crop models - Economic models - Integrated agricultural systems models - Livestock models - Use cases
We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.