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In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al. 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework. [ABSTRACT FROM AUTHOR]

Highlights • APSIM Next Generation enables efficient model development workflow. • An upgraded wheat model is used as a demonstration. • Resultant models are well tested, well documented and easily distributed. • Version control and testing system ensures ongoing model performance. Abstract The process of building and improving a broadly useful set of crop models is a major undertaking and the APSIM (Agricultural Production system SIMulator) development community conduct this work separately from projects that have specific uses of the models. This paper presents a set of standards that a modern crop model should meet, and describes the approaches and software the APSIM development community are using to build and maintain models that meet these standards. The latest version of APSIM combines a range of tools in a single user interface (UI) to assist model developers. It uses a modern version control system to ensure model reliability and a modern distribution system to ensure users can easily access models and receive updates. The wheat model is used as an example to describe the development process using the APSIM platform. Firstly, a test set for the model was developed: • Forty-eight experiments, giving 655 treatments, were collated into a database. • Each of these treatments was configured as a test simulation using the Experiment component in the UI to hasten the process and reduce human error. • The model was implemented in the UI using the plant modelling framework for a visual and flexible approach to model construction. • Graphs and statistics for assessing model performance were constructed in the UI. • From the UI, changes were made to the model, and its performance was reviewed each time until developers were happy with it. • Memo fields were added into the UI to describe experiments and capture the rationale and citations for model structure and parameterisation. This system facilitated a number of science improvements to the wheat model, including new canopy and phenology models and the compilation and more thorough analysis of a large test dataset. The test set was submitted to APSIM governance for review and approval for general release. Once approved, the model was included in the APSIM distribution as a read-only file so users cannot change it. The test set was put into version control to provide a baseline for assessing model performance. Documentation was automatically generated from the test set, providing a full and up-to-date technical description of the model and its evaluation. This demonstrates a robust yet flexible and efficient approach for improving and distributing crop models, facilitated by purpose-designed software. [ABSTRACT FROM AUTHOR]

The assessment of water uptake in agricultural systems commonly relies on the use of process-based biophysical models. To accurately represent crop-soil relationships, these models require local calibration which is often limited by the availability of site-specific data. This is the case for the water extraction coefficient ( kl ) in perennial forage cropping systems. The kl is critical to control water uptake by roots influencing soil moisture dynamics at different soil depths. This crop-soil parameter is particularly challenging to assess in species that establish deep root systems during long periods of regrowth, such as perennial forages. Using three years of detailed field data, we test a method to estimate kl for two perennial crops of broad socio-economic significance (lucerne and perennial ryegrass). The method is based on three physically meaningful parameters: Root Front Velocity (RFV, mm/day), surface kl ( kl 0 ,/day) and the rate of kl decay with soil depth ( λ kl , dimensionless). Our analysis showed that soil volumetric water content dynamics was most sensitive to kl 0 values. A model fitting procedure showed that the highest accuracy of soil volumetric water content ( θ ) simulations was obtained with RFV of 10 mm/day and a kl 0 of 0.11/day for both forages. In contrast, λ kl estimates differed among species, being higher for shallow fibrous ryegrass roots than for the deep lucerne taproots. The analysis also highlighted that multiple parameter set combinations were found to give simulations of acceptable accuracy. The proposed method can be used as a first approximation to parameterise kl when local calibration data is unavailable. However, even for the fitted parameter sets, the accuracy of θ estimates was low at soil depths where there was a substantial transition of soil texture. These insights highlight important aspects to be considered in future development and parameterisation of water uptake models for perennial forages. [ABSTRACT FROM AUTHOR]

Most climate impact assessments for food production simulate single crops with re-initialised soil conditions. However, crop rotations with multiple crops are used in many agricultural regions worldwide. This case-study compares methods to aggregate outputs from simulations of multi-crop systems for climate impact assessments. The APSIM model was used to simulate four crops as monocultures (re-initialised or continuous) or as (single or multiple) instances of continuous rotations. We considered two contrasting climates and two soil types, with four production intensification scenarios (high/low water and nitrogen input). Results suggest that differences among the methods depend on the impact variable of interest and the degree of intensification. Detailed simulations (i.e. multiple runs of continuous rotations) were especially valuable for soil-related variables and limiting growth conditions. These results can indicate sources of uncertainty for large scale impact and adaptation assessments where simplifications of crop rotations are often necessary. [ABSTRACT FROM AUTHOR]

Lucerne (Medicago sativa L.) is a widely grown perennial legume worldwide which can provide high biomass and protein yields, biological N fixation, deep soil water extraction and a range of ecosystems services relevant to current and future agricultural systems. The potential to expand lucerne beyond its current cultivated areas in New Zealand, and its potential productivity across the country's contrasting climate zones, are currently unknown. To gain such insights, we estimated land suitability and spatial distribution of lucerne above-ground biomass across New Zealand lands considering contrasting growth conditions (rain-fed or irrigated for different soils types) and two simulation methods of different complexity (process- and GIS-based approaches). This aimed to assess yield-estimate spatial patterns and sensitivity to model selection for a wide range of combinations of water supply (i.e. irrigation and soil water storage) across New Zealand climate zones. For example, highly suitable areas for lucerne cultivation, were estimated in ∼21 thousand km2 when considering the exclusion of steep slopes, poor soil drainage and excess annual rainfall. The two crop-yield models were applied in response to 30 years of daily historical (1971–2000) weather data downscaled at 5 km resolution on suitable areas. Simulated average lucerne yields ranged from ∼4.5–28 t dry matter/ha per year. Simulations showed a distinct spatial pattern of yield decline from north to south, mainly in response to decreasing temperatures. Temporally, water limited yields were up to 4-fold more variable than under irrigation, depending on the degree of drought stress across different years. Results also unveiled systematic spatial patterns of model uncertainty quantified as yield sensitivity to model selection. For instance, simulated yields were most sensitive to model selection (6–31% of total variability, T i) within high abiotic-stress environments (e.g. low temperature and limited water supply). Overall, soil type selection accounted for most of yield variability (58–78% T i), being particularly important in warmer environments with variable seasonal rainfall regimes (e.g. northern regions). As expected, water supply (i.e. rain-fed or irrigated systems) was relatively more impactful on yield (8–20% T i) for limited rainfall areas, where crops are most drought prone (e.g. east coast and central southern regions). Long-term regional scale comparisons of annual lucerne yield, between 30-year simulated distributions and point-based observations from the AgYields database, helped identify hotspots of yield overestimation. Such insights are useful to guide future research on high yield gap areas (e.g. southern colder and drier locations) and highlight key areas for model improvement (e.g. representation of multiple biotic stresses). Overall, our results provide a first gridded-model assessment of lucerne suitability and yield at national scale and quantify the share of variability explained by key climatic, management and methodological components in spatial analysis studies. These insights can inform future modelling efforts and support agricultural planning that considers the expansion of lucerne and other perennial legumes. • Perennial forage legumes are underrepresented in gridded modelling assessments. • We apply gridded modelling to assess potential to expand lucerne in New Zealand. • A north to south yield decline gradient was found with contrasting model approaches. • Process- and a GIS-based model diverged mostly under multiple/high stress locations. • These results can inform future model improvement efforts and yield-gap analysis. [ABSTRACT FROM AUTHOR]

Early-vigour is a plant trait phenotypically characterised by a rapid expansion of leaf area in early stages of crop growth, before canopy closure. Although early-vigour is already used as a selection criteria in breeding programmes for grain cereals, its genetic variability and potential benefits for oat cover crops is unknown. In this study, we screened 231 oat lines from a commercial forage breeding programme using high-throughput field phenotyping to quantify canopy development rates through aerial photographs. Results showed a wide genetic variability for early-vigour in oats, with canopy cover differences of up to ∼20% during the approximate 30 days period from emergence to full canopy cover. Two oat genotypes with contrasting canopy cover development rates (opposite quartile ranges of the population) were subsequently selected for a detailed investigation of underlying mechanisms explaining early-vigour during two field trials. For these genotypes, the size of individual leaves was found to be the main factor driving differences, with 50% larger leaf area before the sixth leaf from the base of the main tiller in the high early-vigour genotype. A process-based biophysical model (APSIM-NextGen oats) was then parameterised to quantify variability on potential early-vigour benefits across four representative agricultural target environments. This was done by simulating the two selected oat genotypes across 30-years of historical weather data from each of the four distinct climatic zones, considering two contrasting soil types and three possible cover crop sowing dates. Results showed early-vigour to provide overall positive ecosystems services with pooled medians of 7–18% increase in above ground biomass and 5–13% increase in nitrogen uptake which caused a consequent 4–9% reduction in nitrogen leaching losses depending on the location/soil/management combination. Such decline in relative plant-trait effects across different components of the production system (above-ground biomass, above-ground N and N leaching reduction) was also accompanied by increasing variability in responses, with pooled coefficients of variation around 31%, 69% and 80% respectively. Similarly, our results also highlight the relative dilution of trait effects across scales. For instance, a 50–70% difference in basal leaf size at the plant-organ scale caused a 6–10% potential reduction of N leaching at the agricultural system scale. These results highlight the importance of multi-metric evaluations of spatial and temporal variability in trait effects to better inform breeding and selection programmes. Finally, our practical implementation of previously conceptualised methods illustrates the increased depth of understanding about plant-trait benefits when combining interdisciplinary approaches such as high throughput phenotyping, classic crop physiology field experimentation and biophysical modelling. The principles of this approach can be extended to assess the relative value of other traits across different species, managements and environments. • There was phenotypic variability for early-vigour across 231 cover crop oat genotypes. • Early-vigour was mainly explained by a 50–70% larger size of the initial seven leaves. • Long-term simulations showed that biomass and N uptake increased with early-vigour. • This also reduced N leaching but at a smaller magnitude and with greater variability. • Such analysis framework can assess alternative traits in other crops and environments. [ABSTRACT FROM AUTHOR]

The effectiveness of growing winter cover crops to mitigate nitrogen (N) leaching can be widely variable, even within a single climatic zone. We sought to investigate key drivers of this variability for the Canterbury Plains of New Zealand. First, through an analysis of local field experiments, we quantified the local variability in the effectiveness of cover crops to reduce N leaching. We then calibrated and applied a biophysical model to isolate the impact of potential drivers of this variability. Crop management (cover crop sowing date), soil water holding capacity (WHC) and inter-annual weather variability (30 years of historical climate) were selected as main factors to be investigated. The analysis of local literature showed that, compared to fallow treatments, winter cover crops reduced N leaching by an average (±95% CI) of 17 ± 8.2 kg N ha −1 . This represented a median N leaching reduction of ∼50% with a wide interquartile range (6–75%). The modelling study showed that the delay in sowing dates consistently reduced the average effectiveness of cover crops, from >80% for March- to <25% for June-sown crops. For any “sowing date by soil WHC” scenario, there was also a large year to year variability. This was caused by the stochastic effect of inter-annual weather variability on the dynamics of crop N demand and soil N supply. For the conditions assumed in the modelling study, a sensitivity analysis of simulated results showed that sowing dates were the main contributor to total variability in the effectiveness of cover crops, followed by weather, factor interactions and soil WHC. These results suggest the need for caution when interpreting data from individual field trials due to the impact of inter-annual variability and interactions among multiple drivers on cover crop effectiveness. In addition, our analysis highlights the value of complementary methodologies, such as biophysical modelling, for extending the inference space of individual field studies. [ABSTRACT FROM AUTHOR]

We trace the evolution of the representation of management in cropping and grazing systems models, from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules. Attempts to define higher-level organizing concepts in management policies, and to analyse them to identify optimal plans, have focussed on questions relating to grazing management owing to its inherent complexity. “Rule templates” assist the re-use of complex management scripts by bundling commonly-used collections of rules with an interface through which key parameters can be input by a simulation builder. Standard issues relating to parameter estimation and uncertainty apply to management sub-models and need to be addressed. Techniques for embodying farmers' expectations and plans for the future within modelling analyses need to be further developed, especially better linking planning- and rule-based approaches to farm management and analysing the ways that managers can learn. [ABSTRACT FROM AUTHOR]

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics. [ABSTRACT FROM AUTHOR]

• We described several approaches for testing simulation configuration in APSIM. • APSIM input data from 426 experiments across 19 countries were assessed. • We explicitly described the cause of error during the simulation configuration process. • Sources of error were associated with climate, soil, management and observations. • Standard criteria are required to quantify the uncertainties associated with input data. Crop models are usually developed using a test set of data and simulations representing a range of environment, soil, management and genotype combinations. Previous studies demonstrated that errors in the configuration of test simulations and aggregation of observed data sets are common and can cause major problems for model development. However, the extent and effect of such errors using Agricultural Production system SIMulator Next Generation (APSIM) crop models are not usually considered as a source of model uncertainty. This is a methodological paper describing several approaches for testing the APSIM simulation configuration to detect anomalies in the input and observed data. In this study, we assess the simulation configuration process through (i) quality control analysis based on a standardised climate dataset (ii) outlier identification and (iii) a palette of visualization tools. A crop model – APSIM-Potato is described to demonstrate the main sources of error during the simulation configuration and data collation processes. Input data from 426 experiments conducted from 1970 to 2019 in 19 countries were collected and configured to run a model simulation. Plots were made comparing simulation configuration data and observed data across the entire test set so these values could be checked relative to others in the test set and with independent datasets. Errors were found in all steps of the simulation configuration process (climate, soil, crop management and observed data). We identified a surprising number of errors and inappropriate assumptions that had been made which could influence model predictions. The approach presented here moved the bulk of the effort from fitting model processes to setting up broad simulation configuration testing and detailed interrogation to identify current gaps for further model development. [Display omitted] [ABSTRACT FROM AUTHOR]

• The botanical characteristics of chicory and its use as a forage crop were reviewed. • A process-based model for chicory growth was developed using APSIM-PMF. • The chicory model was tested against data from four field experiments. • Model performance was good for biomass and weaker for plant nitrogen content. Chicory has been promoted as an alternative forage crop for livestock farming. It can produce large biomass yields and is highly palatable to animals. However, managing forage chicory in pastoral farms can be challenging because of its growth pattern and low persistence. There is thus a need for modelling tools that can help to understand and manage forage chicory within the farm system. In this work we review the main botanical characteristics of the chicory plant and describe a model developed to simulate the phenology and biomass accumulation of chicory as forage. The model was developed using the Plant Model Framework and it is available for use within the Agricultural Production Systems Simulator. Four experiments with treatments including the use of irrigation, different N fertiliser rates, and defoliation regimes, were used to test the model. We show that the model was able to simulate the biomass accumulation pattern, with R2 values of 0.40-0.54 for standing above-ground biomass and 0.64-0.89 for cumulative harvested material. The performance was weaker when describing plant nitrogen, with R2 of 0.20-0.25 for N content in the above-ground biomass. This indicates that care must be taken when using the model to simulate N balance, and that model refinements are required. For this, more information is needed on the partitioning and mobilisation of N amongst the various organs and on how biomass allocation changes across seasons. [ABSTRACT FROM AUTHOR]