Eline Vanuytrecht

Global sensitivity analysis of yield output from the water productivity model

This study includes a global sensitivity analysis of the water productivity model AquaCrop. The study rationale consisted in a comprehensive evaluation of the model and the formulation of guidelines for model simplification and efficient calibration. The global analysis comprehended a Morris screening followed by a variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) under diverse environmental conditions for maize, winter wheat and rice. The analysis involved twenty-two different climate-crop-soil-meteorology combinations. The main objectives were to distinguish the models influential and non-influential parameters, and to examine the yield output sensitivity. For the AquaCrop model, a number of non-influential parameters could be identified. Making these parameters fixed would be a step towards model simplification. Also, a list of influential parameters was identified. Despite the dependence of parameter ranking on environmental conditions, guiding principles for priority parameters were formulated for calibration in diverse conditions, valuable to model users. For this model that focuses on modelling yield response to water, parameters describing crop responses to water stress were not often among those showing highest sensitivity. Instead, particular root and soil parameters, relevant in the determination of water availability, were influential under various conditions and merit attention during calibration. The considerations made in this study about sensitivity analysis method (Morris vs. EFAST), prior parameter ranges, target functions and ranking variation according to environmental conditions can be extrapolated to other conditions and models, if done with the necessary precaution. Yield output variability of AquaCrop tested with global Morris and EFAST techniques.Morris and EFAST give similar but not identical results.Sensitivity rankings depend on environment, target output and prior parameter range.Guidelines for priority parameters given for calibration in specific settings.Non-influential AquaCrop parameters identified to be fixed for model simplification.

AquaCrop: FAO'S crop water productivity and yield response model *

With the help of relatively few conservative crop parameters, AquaCrop simulates final crop yield in four steps that are easy to understand, which makes the modeling approach transparent. The steps consist in the simulation of development of the green crop canopy cover, crop transpiration, above-ground biomass, and final crop yield. Temperature and water stresses directly affect one or more of the above processes. Nutrient deficiencies and salinity effects are simulated indirectly by moderating canopy cover development over the season, and by reducing crop transpiration and the normalized water productivity. The effect of CO2 concentration on biomass is simulated by altering the normalized water productivity. The model requires a relatively small number of explicit parameter values and mostly intuitive input variables. The paper describes the essence of AquaCrop Version 4.0, applications and parameterization of crops, crop responses to elevated CO2 concentration, soil fertility and salinity, and further model developments.

Regional and global climate projections increase mid-century yield variability and crop productivity in Belgium

The impact of mid-century climatic changes on crop productivity of winter wheat, maize, potato and sugar beet was assessed for a temperate maritime climate in the Flemish Region, Belgium. Climatic projections of multiple regional and global climate models (RCMs from the EU-ENSEMBLES project and GCMs from the Coupled Model Intercomparison Project phase 3) were stochastically downscaled by the LARS-WG weather generator for use in the crop models AquaCrop and Sirius. Primarily positive effects on mean yield were simulated. Crops benefitted from elevated CO2, and from more radiation interception if the cropping period was adapted in response to higher temperatures. However, increased productivity was linked with increased susceptibility to water stress and greater inter-annual yield variability, particularly with adapted management. Impacts differed among and within ensembles of climate models, and among crops and environments. Although RCMs may be more suitable for local impact assessments than GCMs, inter-ensemble differences and contingent wider ranges of impacts with GCM projections found in this study indicate that applying RCMs driven by a limited number of GCMs alone would not give the full range of possible impacts. Further, this study suggests that the simulated intermodel variation can be larger than spatial variation within the region. These findings advocate the use of both GCM and RCM ensembles in assessments where temperature and precipitation are central, such as for crop production.

Improving the use of crop models for risk assessment and climate change adaptation

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk?2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output.3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

A semi-quantitative approach for modelling crop response to soil fertility: evaluation of the AquaCrop procedure

Most crop models make use of a nutrient-balance approach for modelling crop response to soil fertility. To counter the vast input data requirements that are typical of these models, the crop water productivity model AquaCrop adopts a semi-quantitative approach. Instead of providing nutrient levels, users of the model provide the soil fertility level as a model input. This level is expressed in terms of the expected impact on crop biomass production, which can be observed in the field or obtained from statistics of agricultural production. The present study is the first to describe extensively, and to calibrate and evaluate, the semi-quantitative approach of the AquaCrop model, which simulates the effect of soil fertility stress on crop production as a combination of slower canopy expansion, reduced maximum canopy cover, early decline in canopy cover and lower biomass water productivity. AquaCrops fertility response algorithms are evaluated here against field experiments with tef ( Eragrostis tef (Zucc.) Trotter) in Ethiopia, with maize ( Zea mays L.) and wheat ( Triticum aestivum L.) in Nepal, and with quinoa ( Chenopodium quinoa Willd.) in Bolivia. It is demonstrated that AquaCrop is able to simulate the soil water content in the root zone, and the crops canopy development, dry above-ground biomass development, final biomass and grain yield, under different soil fertility levels, for all four crops. Under combined soil water stress and soil fertility stress, the model predicts final grain yield with a relative root-mean-square error of only 11–13% for maize, wheat and quinoa, and 34% for tef. The present study shows that the semi-quantitative soil fertility approach of the AquaCrop model performs well and that the model can be applied, after case-specific calibration, to the simulation of crop production under different levels of soil fertility stress for various environmental conditions, without requiring detailed field observations on soil nutrient content.

Bridging rigorous assessment of water availability from field to catchment scale with a parsimonious agro-hydrological model

While simple crop and hydrological models are limited with respect to the number and accuracy of the processes they incorporate, complex models have high demand for data. Due to the limitations of both categories of models, there is a need for new agro-hydrological models that simulate both crop productivity and water availability in agricultural catchments, with low data and calibration requirements. This study aimed at developing a widely applicable parsimonious agro-hydrological model, AquaCrop-Hydro, which couples the AquaCrop crop water productivity model with a conceptual hydrological model. AquaCrop-Hydro, simulating crop productivity, the daily soil water balance and discharge at the catchment outlet, performed well for an agricultural catchment in Belgium. The model can be used to investigate the effect of agricultural management and environmental changes from field to catchment scale in support of sustainable water management in agricultural areas.