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There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details. info:eu-repo/semantics/acceptedVersion

Climate change will have dire consequences and collaborative efforts are required to quickly develop and assess mitigation solutions. Agriculture is the primary source of the powerful greenhouse gas (GHG) nitrous oxide (N2O) and an important source of GHG emissions. Due to sampling limitations, N2O measurements have traditionally been sparse; approximately 75% of sites we reviewed sampled for fewer than 50 days within a year. Nitrous oxide emissions are highly variable and short-lived peak emission periods may contribute more than 50% to annual emissions. Gap filling around these peaks, if measured at all, can result in poor estimations under the standard practice using area under the curve. Improved gap filling methods that reflect covariate data will likely reduce uncertainty and improve annual N2O estimates. The Global N2O Database was created to serve as a repository for these datasets as well as become a resource for publicly available data and analytical advances.

Global food production is under pressure to produce more from limited resources, with further expectations to reduce waste and pollution and improve social outcomes. Circular economy principles aim to design out waste and pollution, minimise the use of nonrenewable external inputs and increase the lifespan of products and materials. Waste sources on New Zealand farms and options to reduce waste and improve circularity were reviewed. Waste reduction should begin with systems design, while recycling should be at the bottom of the hierarchy. On-farm resource use efficiency has been widely studied, but there are also opportunities to repurpose waste and integrate systems. The use of organic waste products as fertiliser and supplementary feed occurs to some extent, as does use of excess dairy calves in the beef industry, but they present both opportunities and challenges. More farm waste recycling opportunities are becoming available, with new products available from waste processing, such as plastic fence posts. Circular strategies in New Zealand agriculture require more analysis to determine economic, social, cultural and environmental outcomes.

CONTEXT: In May 2020, approximately four months into the COVID-19 pandemic, the journal's editorial team realized there was an opportunity to collect information from a diverse range of agricultural systems on how the pandemic was playing out and affecting the functioning of agricultural systems worldwide. OBJECTIVE: The objective of the special issue was to rapidly collect information, analysis and perspectives from as many regions as possible on the initial impacts of the pandemic on global agricultural systems, The overall goal for the special issue was to develop a useful repository for this information as well as to use the journal's international reach to share this information with the agricultural systems research community and journal readership. METHODS: The editorial team put out a call for a special issue to capture the initial effects of the pandemic on the agricultural sector. We also recruited teams from eight global regions to write papers summarizing the impacts of the first waves of the pandemic in their area. RESULTS AND CONCLUSIONS: The work of the regional teams and the broader research community resulted in eight regional summary papers, as well as thirty targeted research articles. In these papers, we find that COVID-19 and global pandemic mitigation measures have had significant and sometimes unexpected impacts on our agricultural systems via shocks to agricultural labour markets, trade and value chains. And, given the high degree of overlap between low income populations and subsistence agricultural production in many regions, we also document significant shocks to food security for these populations, and the high potential for long term losses in terms of human, natural, institutional and economic capital. While we also documented instances of agricultural system resilience capacities, they were not universally accessible. We see particular need to shore up vulnerable agricultural systems and populations most negatively affected by the pandemic and to mitigate pandemic-related losses to preserve other agricultural systems policy objectives, such as improving food security, or addressing climate change. SIGNIFICANCE: Despite rapid development of vaccines, the pandemic continues to roll on as of the time of writing (early 2022). Only time will tell how the dynamics described in this Special Issue will play out in the coming years. Evidence of agricultural system resilience capacities provides some hopeful perspectives, but also highlights the need to boost these capacities across a wider cross section of agricultural systems and encourage agri-food systems transformation to prepare for more challenges ahead.

Issues of scale pervade every aspect of socio-environmental systems (SES) modeling. They can stem from the context of both the modeling process, and the purpose of the integrated model. A webinar hosted by the National Socio-Environmental Synthesis Center (SESYNC), The Integrated Assessment Society (TIAS) and the journal Socio-Environmental Systems Modelling (SESMO) explored how model stakeholders can address issues of scale. Four key considerations were raised: (1) being aware of our influence on the modeling pathway, and developing a shared language to overcome cross-disciplinary communication barriers; (2) that localized effects may aggregate to influence behavior at larger scales, necessitating the consideration of multiple scales; (3) that these effects are “patterns” that can be elicited to capture understanding of a system (of systems); and (4) recognition that the scales must be relevant to the involved stakeholders and decision makers. Key references in these four areas of consideration are presented to complement the discussion of confronting scale as a grand challenge in socio-environmental modeling. By considering these aspects within the integrated modeling process, we are better able to confront the issues of scale in socio-environmental modeling.

This editorial observes 200 volumes of Agricultural Systems. Volume 1, dated January 1976, began with an article by the inaugural editor, C.R.W. Spedding (1976), that laid the foundations for this journal. We echo that first editorial with this contribution to begin Volume 201 and review some aspects of the history of the journal. To celebrate those 200 volumes, we have prepared a Collection of the highly-cited papers published over the last 46 years in those volumes. Here we present brief summary information about the journal, compare themes common to the opening editorial and the highly-cited papers, and conclude with an intent to review the aims and scope of the journal over the next year or so.

Nitrogen (N) leaching losses from grazed pasture systems pose a risk to the environment with mitigation strategies urgently required to achieve regulatory limits. There has been increased interest ...

The effectiveness of Integrated Water Resource Management (IWRM) modeling hinges on the quality of practices employed through the process, starting from early problem definition all the way through to using the model in a way that serves its intended purpose. The adoption and implementation of effective modeling practices need to be guided by a practical understanding of the variety of decisions that modelers make, and the information considered in making these choices. There is still limited documented knowledge on the modeling workflow, and the role of contextual factors in determining this workflow and which practices to employ. This paper attempts to contribute to this knowledge gap by providing systematic guidance of the modeling practices through the phases (Planning, Development, Application, and Perpetuation) and steps that comprise the modeling process, positing questions that should be addressed. Practice-focused guidance helps explain the detailed process of conducting IWRM modeling, including the role of contextual factors in shaping practices. We draw on findings from literature and the authors’ collective experience to articulate what and how contextual factors play out in employing those practices. In order to accelerate our learning about how to improve IWRM modeling, the paper concludes with five key areas for future practice-related research: knowledge sharing, overcoming data limitations, informed stakeholder involvement, social equity and uncertainty management.

Grasslands comprised of grass-legume mixtures could become a substitute for nitrogen fertiliser through biological nitrogen fixation (BNF) which in turn can reduce nitrous oxide emissions directly from soils without negative impacts on productivity. Models can test how legumes can be used to meet environmental and production goals, but many models used to simulate greenhouse gas (GHG) emissions from grasslands have either a poor representation of grass-legume mixtures and BNF, or poor validation of these features. Our objective is to examine how such systems are currently represented in two process-based biogeochemical models, APSIM and DayCent, when compared against an experimental dataset with different grass-legume mixtures at three nitrogen (N) fertiliser rates. Here, we propose a novel approach for coupling DayCent, a single species model to APSIM, a multi-species model, to increase the capability of DayCent when representing a range of grass-legume fractions. While dependent on specific assumptions, both models can capture the key aspects of the grass-legume growth, including biomass production and BNF and to correctly simulate the interactions between changing legume and grass fractions, particularly mixtures with a high clover fraction. Our work suggests that single species models should not be used for grass-legume mixtures beyond about 30% legume content, unless using a similar approach to that adopted here.

In this work we compare the performance of machine learning metamodels of different scale for the prediction of pasture grass nitrogen response rate using a case study across different locations in New Zealand. We first used a range of soil, plant and management parameters known to affect grass growth and/or nitrogen response. These generated a complete factorial that enabled us to run virtual nitrogen response rate experiments, using the APSIM simulation model, in eight locations around the country. We included 40 years of weather data to capture the effect of weather variability on response rate. This created a large database with which to train machine learning models. We created local, regional, and nation-wide models using Random Forest and tested them on known and unknown locations. To evaluate the models, we first calculated the RMSE, MAE and R2 and then determined if the distributions of the predictions were statistically different using the Mann-Whitney U test. Finally, we explore the generalizability of the models using the error metrics and the results of the statistical test.

International audience

Funding Information: This modeling study was a joint effort of the Models4Pastures project within the framework of FACCE-JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE-JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc. Mobility fellowship (SNSF funded project: P1EZP2_172121). Lutz Merbold further acknowledges the support received for CGIAR Fund Council, Australia (ACIAR), Irish Aid, the European Union, the Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as well as for the CGIAR Research Program on Livestock. The NZ contributors acknowledge funding from the New Zealand Government Ministry of Primary Industries to support the aims of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) research program). The UK partners acknowledge funding by DEFRA and the RCUK projects: N-Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli, Camilla Dibari, and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from CN-MIP project funded by the French National Research Agency (ANR-13-JFAC-0001) and from ADEME (no. 12-60-C0023). Open access funding enabled and organized by Projekt DEAL Funding Information: This modeling study was a joint effort of the Models4Pastures project within the framework of FACCE‐JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE‐JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc. Mobility fellowship (SNSF funded project: P1EZP2_172121). Lutz Merbold further acknowledges the support received for CGIAR Fund Council, Australia (ACIAR), Irish Aid, the European Union, the Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as well as for the CGIAR Research Program on Livestock. The NZ contributors acknowledge funding from the New Zealand Government Ministry of Primary Industries to support the aims of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) research program). The UK partners acknowledge funding by DEFRA and the RCUK projects: N‐Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli, Camilla Dibari, and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from CN‐MIP project funded by the French National Research Agency (ANR‐13‐JFAC‐0001) and from ADEME (no. 12‐60‐C0023). Open access funding enabled and organized by Projekt DEAL Publisher Copyright: ©2020. The Authors. Open access funding enabled and organized by Projekt DEAL

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

International audience

Irrigation is a useful crop enhancement procedure up to the point where free surface water appears. Thereafter, water can begin to flow into waterways, leaching nutrients and giving rise to environmental damage, as well as being a waste of a precious resource. The current work addresses the problem of measuring free water on the surface of agricultural soils by a real-time acoustic remote sensing method. Directional acoustic transmitter and receiver arrays are used to define a ‘footprint’ on the ground from which changes in reflectance are sensed. These arrays are mounted on a moving irrigator. Chirp signals are used to provide along-path resolution and to ensure robustness against unwanted acoustic background noise from farm machinery and the irrigator. Field measurements have been conducted above a well-defined ‘quadrat’ with controlled and measured water content, and also with the instrument mounted on an operational irrigator. A structured light camera mounted above the footprint is used to validate surface water fraction. It is found that the areal fraction of free water on the soil surface can be reliably estimated from changes in the amplitude of the reflected sound waves. The mechanism giving rise to the observed acoustic reflectivity changes is discussed and a model is developed which agrees with normalized intensity observations with a coefficient of determination R2 between 0.65 and 0.83. The rms error between model predictions and observations is comparable to the rms variation of the measurements, indicating that there is insignificant error due to the choice of model.

The COVID-19 pandemic has resulted in immediate, serious, and worldwide human health issues. Necessary counter measures to the virus, e.g. quarantines and other restrictions, will remain in place for many months and have uncertain end dates. International efforts to control the virus by limiting human movement is inevitably causing economic shocks and social costs that will affect the functioning of agricultural and food systems worldwide. We are already witnessing the indirect effects of the pandemic on agricultural systems across the globe. Massively decreased demand for restaurant and commercial food services in combination with restrictions in labour, processing capacity and/or storage has led to farmers discarding their output en masse. Quarantine measures are severely affecting labour availability for key time-critical farming from sowing vegetable crops to picking fruit. As the crisis develops, these impacts are likely to become more widely and deeply felt in agricultural sectors and national economies. The significance and severity of the pandemic, and its likely impact on agriculture worldwide, calls for substantial reflection in both the short- and long-term. We need to understand the immediate consequences for the global network of agricultural and food systems on which we rely so heavily. We should track unexpected risks, weaknesses and systemic shifts to understand short-term effects as well as those that may be long-lasting or permanent.

Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs - C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods). info:eu-repo/semantics/acceptedVersion

Irrigation of crops and grazed pastures can lead to harmful losses of nutrients via overland flow across the edge of the field. While good irrigation design can assist with avoiding overland flow, soil surface conditions can change rapidly and lead to surface flow even under well-designed irrigation systems. Therefore, real-time methods to detect emerging flow conditions, early enough to prevent substantial flow from the field during irrigation, is a potential mitigation option. But these methods require a prediction of the initiation of overland flow conditions in order to make the connection with real-time observations.On a naturally-rough agricultural soil, triggering of overland flow is primarily related to the process of gradual filling of small (~50 mm across) depressions. As depressions fill, hydraulic connections are established with their neighbours and this eventually leads to sufficient connectivity that overland flow is initiated. The initiation of overland flow generally occurs at a critical value of connectivity (COF); the proportion of the soil surface that is connected via a water-filled pathway to an exit point of the field. As water ponding in, and flowing through, local depressions increases, the COF of the field increases and this leads to flow across the field boundaries. Quantifying the development of COF during an irrigation event, therefore, is key to predicting the initiation of overland flow.We propose a method to continuously monitor the development of COF during an irrigation event that requires two elements. The first is a new proximal sensing technique, which exploits acoustic technology to continuously monitor Asw, the proportion of the soil surface covered in water. The acoustic method comprises directional acoustic transmitter and receiver arrays. The directionality of the arrays provides a well-defined footprint area on the ground beneath the instrument. The Asw can be reliably estimated from changes in the amplitude of reflected sound waves. The second element is a ponding and redistribution model which simulates the flow of water over a rough soil surface and assists by converting Asw into COF.Our preliminary results show that this real-time method of monitoring COF has a considerable scope in a variety of environments where prediction of overland flow initiation is desirable.

Context Since COVID-19 (SARS-CoV-2) was first identified in the human population, it has had immediate and significant effects on peoples' health and the worldwide economy. In the absence of a vaccine, control of the virus involved limiting its spread through restrictions in the movement of people, goods and services. This has led to unprecedented impacts on labour availability, provision of goods and services, value chains, and markets. Objective Against the backdrop of COVID-19 control measures, this article summarises quantitative and qualitative assessments of the impacts, adaptations, and opportunities to increase the resilience of the agricultural systems in Australia and New Zealand. Methods Using both survey and interview methodologies, we describe the various agri-food systems and the impacts of the COVID-19 control measures across different industries, and discuss the results applying a resilience framework. Results As essential services, all agricultural activities except for fibre production have been permitted to continue during quarantine periods but have been exposed to the major flow-on effects of movement control. We found that, to June 2020, the impacts of the COVID-19 control measures on the agri-food sectors in both Australia and New Zealand have been relatively small and that this has been due to the high levels of resilience in the agricultural systems and the people running them. Conclusions We consider agri-food systems to be comprised of multiple subsystems with varying vulnerability to external influences. Agri-food systems were resilient to June 2020 at least, and that resilience was achieved via one or more subsystems that were able to compensate for the more vulnerable subsystems. We contrast the resilience of industries that have high plasticity (that can have a flow of material that can safely vary in time) to more rigid industries that are dependent on a steady flow of material with little or no storage. Ultimately both types of industries were resilient, but they achieved that resilience via compensating subsystems. High plasticity industries relied on their production and processing subsystem; rigid industries engaged their institutional subsystem to achieve the same end. The social and cultural subsystem was important across all industries. Significance It is not yet clear if the current resilience mechanisms can persist under the continued onslaught of the virus. We indicate the need to capture longer term effects and analysis during the more sustained effects of the virus and through a recovery period. We anticipate a follow-up study in 2022.

Soil surface roughness controls how water ponds on and flows over soil surfaces. It is a crucial parameter for erosion and runoff studies. Surface roughness has traditionally been measured using manual techniques that are simple but laborious. Newer technologies have been proposed that are less laborious but require expensive equipment and considerable expertise. New depth-camera technologies might provide a useful alternative. We tested the ability of one such camera to measure soil surface roughness. The camera's accuracy was good but decreased with camera-soil distance (0.3% at 750 mm and 0.5% at 1500 mm) however it was very precise (< 0.5 mm for elevation and < 0.05 mm for random roughness). Similarly, the error of the surface area estimation increased with camera-soil distance (0.56% at 750 mm and 2.3% at 1500 mm). We describe the workflow to produce high-resolution digital elevation models from initial images and describe the conditions under which the camera will not work well (e.g. extremes of lighting conditions, inappropriate post-processing options). The camera was reliable, required little in the way of additional technology and was practical to use in the field. We propose that depth cameras are a simple and inexpensive alternative to existing techniques. • We tested a commercially-available 3D depth camera. • The camera gave highly accurate and precise soil surface measurements. • The camera provides an inexpensive alternative to existing techniques., Graphical abstract Image, graphical abstract

Nitrate leaching from urine deposited by grazing animals is a critical constraint for sustainable dairy farming in New Zealand. While considerable progress has been made to understand the fate of nitrogen (N) under urine patches, little consideration has been given to the spread of urinary N beyond the wetted area. In this study, we modelled the lateral spread of nitrogen from the wetted area of a urine patch to the soil outside the patch using a combination of two process-based models (HYDRUS and APSIM). The simulations provided insights on the extent and temporal pattern for the redistribution of N in the soil following a urine deposition and enabled investigating the effect of lateral spread of urinary N on plant growth and N leaching. The APSIM simulation, using an implementation of a dispersion-diffusion function, was tested against experimental data from a field experiment conducted in spring on a well-drained soil. Depending on the geometry considered for the dispersion-diffusion function (plate or cylindrical) the area-averaged N leaching decreased by 8 and 37% compared with simulations without lateral N spread; this was due to additional N uptake from pasture on the edge area. A sensitivity analysis showed that area-averaged pasture growth was not greatly affected by the value of the dispersion factor used in the model, whereas N leaching was very sensitive. Thus, the need to account for the edge effect may depend on the objective of the simulations. The modelling results also showed that considering lateral spread of urinary N was sufficient to describe the experimental data, but plant root uptake across urine patch zones may still be relevant in other conditions. Although further work is needed for improving accuracy, the simulated and experimental results demonstrate that accounting for the edge effect is important for determining N leaching from urine-affected areas.

From 1990, the Agricultural Production Systems sIMulator (APSIM) has grown from a field-focused farming systems framework used by a small number of people, into a large collection of models used by thousands of modellers internationally. The software grew to consist of several hundred thousand lines of code in multiple programming languages. This has led to a large, complex software ecosystem that is difficult to maintain. In addition, systems modellers increasingly require software systems that integrate multiple disciplines, can represent evermore complex farming systems, can run on multiple operating systems (desktop, web, mobile), can operate at or be adjusted to multiple temporal and spatial scales (field, farm, region, continent, global) and run faster for larger simulation analyses. This is difficult to achieve in an aging framework.For these reasons, the APSIM Initiative is building the next generation of APSIM. This manuscript outlines the approach taken and lessons learnt. APSIM is an agricultural modelling framework used extensively worldwide.APSIM Next Generation is a new, modern rewrite of APSIM.Many lessons have been learnt during this rewrite.A good software process is important in all model development activities.

© 2017 Elsevier Ltd Crops and livestock play a synergistic role in global food production and farmer livelihoods. Increasingly, however, crops and livestock are produced in isolation, particularly in farms operating at the commercial scale. It has been suggested that re-integrating crop and livestock systems at the field and farm level could help reduce the pollution associated with modern agricultural production and increase yields. Despite this potential, there has been no systematic review to assess remaining knowledge gaps in both the social and ecological dimensions of integrated crop and livestock systems (ICLS), particularly within commercial agricultural systems. Based on a multi-disciplinary workshop of international experts and additional literature review, we assess the current knowledge and remaining uncertainties about large-scale, commercial ICLS and identify the source of remaining knowledge gaps to establish priorities for future research. We find that much is understood about nutrient flows, soil quality, crop performance, and animal weight gain in commercial ICLS, but there is little knowledge about its spatial extent, animal behavior or welfare in ICLS, or the tradeoffs between biodiversity, pest and disease control, greenhouse gas (GHG) mitigation, and drought and heat tolerance in ICLS. There is some evidence regarding the economic outcomes in commercial ICLS and supply chain and policy barriers to adoption, but little understanding of broader social outcomes or cultural factors influencing adoption. Many of these knowledge gaps arise from a basic lack of data at both the field and system scales, which undermines both statistical analysis and modeling efforts. Future priorities for the international community of researchers investigating the tradeoffs and scalability of ICLS include: methods standardization to better facilitate international collaborations and comparisons, continued social organization for better data utilization and collaboration, meta-analyses to answer key questions from existing data, the establishment of long term experiments and surveys in key regions, a portal for citizen science, and more engagement with ICLS farmers.

Despite the importance of soil physical properties on loss pathways from high nitrogen (N) inputs quantitative information on the effect of soil heterogeneity on the fate of N is lacking. To quantify how changes in the physical description of soil layers, used as model inputs, affect various outputs of the biophysical APSIM model regarding the fate of urinary nitrogen (N) following winter grazing, a modelling sensitivity analysis was performed. APSIM was set up according to a field experiment on a poorly drained Flaxton soil in the Canterbury region of New Zealand. Soil profile descriptions were based on various measured datasets, with and without compacted soil surface layers due to treading during grazing of the winter forage crop. The effect of the inclusion of more permeable sandy layers within the subsoil, a common occurrence in the study region, was also examined. By systematically replacing soil layers with different physical properties, the effect of these layers on the N loss pathways was evaluated. The analysis identified that the bottom layer (1–1.5 m depth) had the greatest impact. With a low-permeability bottom layer nitrate leaching was 20% lower, whereas pasture N uptake, denitrification and nitrous oxide emissions were greater (by 5%, 58% and 43%). This is due to the effect of the bottom layer on water movement and storage within the soil profile. This description of the bottom layer had a substantially greater effect than compaction of the top soil layers due to treading. These results highlight the need for accurate soil physical descriptions for adequate model parameterisation, when such models are used for assessing management practices aimed at reducing environmental impacts.

We sought to extend the spatial scale of soil-plant models by including, rather than ignoring, heterogeneity using the deposition of urine patches as an example. Our pseudo-patches approach preserves the most important biophysical effects but is computationally-tractable within a multi-paddock simulation. It explicitly preserves the soil carbon and nitrogen heterogeneity but does not require independent simulation of soil water and plant processes and is temporal in that the patches of heterogeneity can appear and disappear during the simulation. The approach was tested through comparison to simulations that more-closely represented field conditions and which contained independent urine patches. The testing was successful, reducing substantial error in the simulation of pasture grazed and leaching for modest increases in simulation execution time but we recommend additional testing under very low and very high stocking densities. The approach is applicable to any heterogeneity in soil nitrogen or carbon such as in spatially-managed fertiliser applications. Display Omitted Most crop-soil models operate at a notional patch scale and ignore spatial heterogeneity.Inclusion of heterogeneity presents scientific, technical and practical problems.We present a method to increase the spatial scale by including some forms of heterogeneity.Urine patches drive many soil-plant processes but are usually ignored in simulation models.We apply this method to the case of urine patches in grazed pastures.

Measurements of nitrous oxide (N

On rough agricultural soils, initiation of overland flow is primarily related to the gradual filling of small depressions. As the volume of water ponding in local depressions increases, the connectivity of those depressions increases, and that connectivity permits flow across the field boundaries. Previous studies have aimed at predicting overland flow connectivity by means of depression storage, but this is exceedingly difficult to quantify during an irrigation or rainfall event. There has been little work linking overland flow connectivity through variables that are more readily-measured than depression storage, such as the proportion of the soil surface covered in water (ASW). We propose using ASW as a proxy for hydrological connectivity, which can be measured using proximal remote sensing. A series of overland flow experiments were conducted in two contrasting plots of ~1.5 m2. Outlet discharge and changes in soil surface covered in water were continuously recorded during the experiments. The experiments demonstrated that the rougher soil surface experienced a delayed initiation of overland flow. The results also showed overland flow initiation was characterised by a distinct connectivity threshold and showed a clear response of overland flow to ASW. We further investigated the hydrological connectivity in twenty-one additional agricultural soils with contrasting micro-topographic conditions. Our results suggest that a predictor based on ASW has substantial potential to predict initiation of overland flow. The prediction is sufficiently early that it could be used in modern variable-rate irrigation systems, in combination with a sensor to measure ASW in real time, to prevent substantial flow from fields during irrigation.

Highlights • Introducing the models and user interface for characterising a soil in APSIM simulations. • Listing and describing the parameters needed for building soil descriptions in APSIM. • Providing recommendations for good practice when setting up soil parameters in APSIM., Soil processes have a major impact on agroecosystems, controlling water and nutrient cycling, regulating plant growth and losses to the wider environment. Process-based agroecosystem simulation models generally encompass detailed descriptions of the soil, including a wide number of parameters that can be daunting to users with a limited soil science background. In this work we review and present an abridged description of the models used to simulate soil processes in the APSIM (Agricultural Production Systems sIMulator) framework. Such a resource is needed because this information is currently spread over multiple publications and some elements have become outdated. We list and briefly describe the parameters, and establish a protocol with guidelines, for building a soil description for APSIM. This protocol will promote consistency, enhancing the quality of the science done employing APSIM, and provide an easier pathway for new users. This compilation should also be of relevance to users of other models that require detailed soil information.•This paper presents a brief description of the models for simulating soil processes in the APSIM model.•The method stablishes guidelines to define the parameters for building a soil description for APSIM., Graphic abstract Image, graphical abstract

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.

Replacing fertilizer nitrogen with biological nitrogen fixation (BNF) through legumes has been suggested as a strategy for nitrous oxide (N 2 O) mitigation from intensively managed grasslands. While current literature provides evidence for an N 2 O emission reduction effect due to reduced fertilizer input, little is known about the effect of increased legume proportions potentially offsetting these reductions, i.e. by increased N 2 O emissions from plant residues and root exudates. In order to assess the overall effect of this mitigation strategy on permanent grassland, we performed an in-situ experiment to quantify net N 2 O fluxes and biomass yields in two differently managed grass-clover mixtures. We measured N 2 O fluxes in an unfertilized parcel with high clover proportions vs. a fertilized control parcel with low clover proportions using the eddy–covariance (EC) technique over two years. Furthermore, we related the measured N 2 O fluxes to management and environmental drivers. To assess the effect of the mitigation strategy, we measured biomass yields and quantified biologically fixed nitrogen using the 15 N natural abundance method. The mitigation management effectively reduced N 2 O emissions by 54 % and 39 % in 2015 and 2016, respectively. These reductions in N 2 O emissions can be attributed to the absence of fertilization on the clover parcel. Differences in clover proportions during periods with no recent management showed no measurable effect on N 2 O emissions, indicating that decomposition of plant residues and rhizodeposition did not compensate the effect of fertilizer reduction on N 2 O emissions. Annual biomass yields were similar under mitigation management, resulting in a reduction of N 2 O emission intensities from 0.42 g N 2 O-N kg −1 DM (control) to 0.28 g N 2 O-N kg −1 DM (clover parcel) over the two years observation period. We conclude that N 2 O emissions from fertilized grasslands can be effectively reduced without losses in yield by increasing the clover proportion and reducing fertilization.

Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: −64 ± 74 g C m−2 yr−1 (animal density reduction) and −81 ± 74 g C m−2 yr−1 (N and animal density reduction), against the baseline of −30.5 ± 69.5 g C m−2 yr−1 (LSU [livestock units] ≥ 0.76 ha−1 yr−1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m−2 yr−1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU−1 yr−1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.

International audience; Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2O)emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents.Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grass-land growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields(from 36% at stage 1 down to 4% on average) and grassland productivity (from 44%to 27%) and to a lesser and more variable extent for N2O emissions. Yield-scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed.

During the past decade, the application of agricultural production systems modelling has rapidly expanded while there has been less emphasis on model improvement. Cropping systems modelling has become agricultural modelling, incorporating new capabilities enabling analyses in the domains of greenhouse gas emissions, soil carbon changes, ecosystem services, environmental performance, food security, pests and disease losses, livestock and pasture production, and climate change mitigation and adaptation. New science has been added to the models to support this broadening application domain, and new consortia of modellers have been formed that span the multiple disciplines.There has not, however, been a significant and sustained focus on software platforms to increase efficiency in agricultural production systems research in the interaction between the software industry and the agricultural modelling community. This paper describes the changing agricultural modelling landscape since 2002, largely from a software perspective, and makes a case for a focussed effort on the software implementations of the major models. The agricultural modelling community has broadened its scientific focus over the last decade.The software implementations of the leading agricultural models hasn't changed significantly in the last decade.A focussed effort on agricultural modelling software and process is needed.

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. Representing farm management using scripts of rules is powerful and flexible."Rule templates" combine this power with ease of use for occasional model users.We trace the history of this modelling technique and its software implementation.Management sub-models face issues of parameter estimation and uncertainty.Management rules can be used to represent learning and enhance farm typologies.

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. APSIM is an agricultural modelling framework used extensively worldwide.It can simulate a wide range of agricultural systems.It begins its third decade evolving into an agro-ecosystem framework.

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.

Soil organic carbon (SOC) is an important and manageable property of soils that impacts on multiple ecosystem services through its effect on soil processes such as nitrogen (N) cycling and soil physical properties. There is considerable interest in increasing SOC concentration in agro-ecosystems worldwide. In some agro-ecosystems, increased SOC has been found to enhance the provision of ecosystem services such as the provision of food. However, increased SOC may increase the environmental footprint of some agro-ecosystems, for example by increasing nitrous oxide emissions. Given this uncertainty, progress is needed in quantifying the impact of increased SOC concentration on agro-ecosystems. Increased SOC concentration affects both N cycling and soil physical properties (i.e., water holding capacity). Thus, the aim of this study was to quantify the contribution, both positive and negative, of increased SOC concentration on ecosystem services provided by wheat agro-ecosystems. We used the Agricultural Production Systems sIMulator (APSIM) to represent the effect of increased SOC concentration on N cycling and soil physical properties, and used model outputs as proxies for multiple ecosystem services from wheat production agro-ecosystems at seven locations around the world. Under increased SOC, we found that N cycling had a larger effect on a range of ecosystem services (food provision, filtering of N, and nitrous oxide regulation) than soil physical properties. We predicted that food provision in these agro-ecosystems could be significantly increased by increased SOC concentration when N supply is limiting. Conversely, we predicted no significant benefit to food production from increasing SOC when soil N supply (from fertiliser and soil N stocks) is not limiting. The effect of increasing SOC on N cycling also led to significantly higher nitrous oxide emissions, although the relative increase was small. We also found that N losses via deep drainage were minimally affected by increased SOC in the dryland agro-ecosystems studied, but increased in the irrigated agro-ecosystem. Therefore, we show that under increased SOC concentration, N cycling contributes both positively and negatively to ecosystem services depending on supply, while the effects on soil physical properties are negligible.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N

Intensification of pastoral dairy systems often means more nitrogen (N) leaching. A number of mitigation strategies have been proposed to reduce or reverse this trend. The main strategies focus on reducing the urinary N load onto pastures or reducing the rate of nitrification once the urine has been deposited. Restricted grazing is an example of the former and the use of nitrification inhibitors an example of the latter. A relevant concern is the cost effectiveness of these strategies, independently and jointly. To address this concern, we employed a modeling approach to estimate N leaching with and without the use of these mitigation options from a typical grazing dairy farm in New Zealand. Three restricted grazing options were modeled with and without a nitrification inhibitor (dicyandiamide, DCD) and the results were compared with a baseline farm (no restricted grazing, no inhibitor). Applying DCD twice a year, closely following the cows after an autumn and winter grazing round, has the potential to reduce annualized and farm-scale N leaching by ∼12%, whereas restricted grazing had leaching reductions ranging from 23 to 32%, depending on the timing of restricted grazing. Combining the two strategies resulted in leaching reductions of 31 to 40%. The abatement cost per kilogram of N leaching reduction was NZ$50 with DCD, NZ$32 to 37 for restricted grazing, and NZ$40 to 46 when the two were combined. For the range analyzed, all treatments indicated similar cost per percentage unit of mitigated N leaching, demonstrating that restricted grazing and nitrification inhibitors can be effective when used concurrently.

A multi-model assessment of C cycling and soil C sequestration in grasslands and croplands. 6th International Symposium on Soil Organic Matter ; Harpenden (Royaume Uni)