CONTEXT Over the last 26 years, researchers globally have successfully applied the soil nitrogen (N) model in the Agricultural Production Systems sIMulator (APSIM) to simulate N cycling and its effects on crop production across a range of agricultural systems and environments. As the modelling community further expands its focus to include environmental impacts of farming, it needs the model to be fit for this broader purpose. OBJECTIVE Accurately modelling N loss via different pathways demands more of the model and so, to inform and prioritise future development needs, we embarked on a detailed review of APSIM's soil N modelling capability. METHODS We conducted a comprehensive search of APSIM Soil N model verification studies and found 131 relevant publications across a wide range of systems, applications, and processes. We examined their approaches and findings, and distilled out the lessons learnt. RESULTS AND CONCLUSIONS The model-data comparisons showed strong performance across all modelled processes, despite limited changes to the core of the soil N model since its inception. The model's relatively simple conceptual pool approach to modelling carbon (C) dynamics with N cycling linked via C:N ratios, has proven remarkably versatile. However, these conceptual pools have posed challenges relating to initialisation methods and the resulting sensitivity of predictions at different time scales, e.g. long-term C trajectories vs. short-term seasonal N dynamics. Correctly predicting timing of N loss on a daily timestep also proved challenging, but this level of resolution may not always be required. APSIM's adaptable code structure facilitated the creation of model prototypes (e.g., ammonia volatilisation and N in runoff) allowing testing of different conceptualisations ahead of formal release. SIGNIFICANCE APSIM is one of the most widely used agricultural systems models. This review, which covers model documentation, model-data comparisons, various approaches to parameterisation, and prototypes for additional processes, consolidates decades of research into insights about the model and its functioning. The review highlights the importance of model evaluations across a wide range of applications to ensure model robustness, to identify issues that may be masked in single studies, and to allow the emergence of solutions with broad applicability.

Context The ability of soils to contribute to greenhouse gas mitigation requires the stock of carbon to be increased in the long term. Studies have demonstrated the potential of soils to increase in carbon at global to regional scales, with soil mineral surface area a key factor to this potential. However, there is limited knowledge on the distribution of mineral surface area and whether the distribution of soil carbon sequestration potential varies at the farm scale. Aims The aim of this study was to evaluate the spatial variability in mineral surface area and sequestration potential of SOC at a farm scale. Methods We used a case study farm to apply existing published methodology and assess the spatial distribution of the mineral surface area, the maximum amount of stable carbon that a soil could hold, and the subsequent potential for soil carbon sequestration at the farm scale. A total of 200 samples were collected across the farm using a balance accepted sampling design prior to analysis for total carbon, mineral surface area, and sequestration potential. Key results Despite being in a localised area, the farm demonstrated that the distributions of mineral surface area and total carbon were related to variation in the underlying soil type. When data were examined spatially, there were areas within the farm that had greater potential to stabilise more carbon and also regions where there were greater carbon stocks. Conclusions The spatial distribution of SOC, mineral surface area, and potential to increase MAOC was well represented by the spatial distribution of soil type within a farm. This case study demonstrated areas within the farm that had potential to increase the MAOC fraction. Implications This case study offers an approach that would give farmers and land managers knowledge to improve the understanding of the carbon dynamics across their farm and to identify areas that have greater potential to contribute to greenhouse gas mitigation and the areas that would be more susceptible to soil carbon loss. Using this approach could allow targeted management practices to be applied to specific regions on-farm to either increase soil carbon or protect existing stocks.

Spiders contribute to pest suppression in agroecosystems by direct and non-direct consumption. They provide an ecosystem service which provides economic gains to horticultural growing systems, such as apples, wine grapes, and kiwifruit. Very few studies on spider biodiversity in cropping systems have been completed in New Zealand, and no studies have been published for New Zealand orchard systems. In this study, spiders and harvestmen were sampled from vineyards, apple orchards, and kiwifruit orchards in three New Zealand locations, Waipara, Motueka, and Kerikeri. Spiders were sampled using pitfall traps, sweep netting, active day sampling, and active night sampling. A total of 1359 spiders and 87 Opiliones were caught in this study, from 17 families and 31 species. Sixteen of the 31 (51.6%) species found were introduced, 9 (29%) endemic to New Zealand, two species (6.4%) native to New Zealand, and four (12.9%) unknown. There were five dominant spider families caught (Araneidae, Lycosidae, Theridiidae, Linyphiidae, and Desidae), and of the adults, there were five dominant species: Anoteropsis hilaris, Tenuiphantes tenuis, Cryptachaea veruculata, Cryptachaea blattea, and Steatoda capensis. This study provides the first important step in describing the spider families and species found in three economically important New Zealand horticultural systems.

Soil structure provides a home to large numbers of microorganisms offering them a food base, support, access to water, air and nutrients and protection from predators. To be able to function, soils need to deliver these essential requirements for life, at micro-habitat scales. Soil structure is the soil characteristic that makes this possible. Therefore, soil structure holds the key to life in soil, regulates many ecosystem services and ultimately underpins sustainable life on Earth. Despite this, the exact way in which soil structure exerts its control is not fully understood and considered to be too complex to be explicitly included in modelling of key processes such as SOM dynamics

Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development.

Context The performance of process-based agroecosystem simulation models is highly sensitive to the numerous input parameters, many associated with high variability and uncertainty. Aims Our aims were to: (1) test the accuracy of the Agricultural Production Systems sIMulator (APSIM) model regarding the prediction of soil water storage and movement in a pasture system with a free draining pumice soil based on site-specific soil hydraulic properties; (2) identify sensitive soil hydraulic properties on model outputs; and (3) identify the influence of uncertainty in the description of soil properties on various model outputs. Methods We carried out a sensitivity analysis (SA) to identify sensitive soil hydraulic parameters. We set up APSIM to simulate a pasture system on a free-draining pumice soil in New Zealand. The model was first established with site-specific soil hydraulic properties and outputs were compared with measured soil moisture status and drainage. Next, the model’s sensitivity to the soil hydraulic parameters was assessed for various outputs linked to production and environmental outcomes. Key results Varying the various hydraulic parameters affected soil moisture status, but it had generally little effect on drainage, N leaching, and pasture production in this system. Conclusions The results suggest that for well-drained soils in a high precipitation zone with no water limitation, the model has low sensitivity to soil hydraulic parameters. Further analysis is required for different soils and for drier conditions. Implications For well-drained soils and under non-limiting water conditions the use of general data from databases, rather than site specific measurement of hydraulic properties is justified.

Agroecosystem models have become an important tool for impact assessment studies, and their results are often used for management and policy decisions. Soil information is a key input for these models, yet site-specific soil property data are often not available, and soil databases are increasingly being used to provide input parameters. For New Zealand, the digital spatial soil information system S-map provides geospatial data on a range of soil characteristics, including estimates of soil water properties. We describe a protocol for how properties from S-map can be used as input parameters for the APSIM (Agricultural Production Systems sIMulator) framework. Finally, we investigate how changes in the physical description of soil layers, and soil organic matter pools, affect the various outputs of APSIM.•This paper presents a description of how information from S-map, a digital soil map of New Zealand, can be used for building a soil description for APSIM.•A sensitivity analysis shows the effect of soil layering and the set-up setup, size, and distribution of SOM pools on model outputs, including plant growth and N leaching.

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.

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 (Int(C) = -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).

There is a well-recognized need for improved fractionation methods to partition soil organic matter into functional pools. Physical separation based on particle size is widely used, yielding particulate organic matter (POM, i.e., free or “uncomplexed” organic matter > 50 μm) as the most labile fraction. To evaluate whether POM meets criteria for an ideal model pool, we examined whether it is: 1) unique, i.e., found only in the > 50 μm fraction and 2) homogeneous, rather than a composite of different subfractions. Following ultrasonic dispersion, sand (> 50 μm) along with coarse (20–50 μm) and fine (5–20 μm) silt fractions were isolated from a silt loam soil under long-term pasture at Lincoln, New Zealand. The sand and silt fractions contained 20% and 21% of total soil C, respectively. We adopted a sequential density separation procedure using sodium polytungstate with density increasing step-wise from 1.7 to 2.4 g cm−3 to recover organic matter (light fractions) from the sand and silt fractions. Almost all (ca. 90%) the organic matter in the sand fraction and a large proportion (ca. 60%–70%) in the silt fractions was recovered by sequential density separation. The results suggested that POM is a composite of organo-mineral complexes with varying proportions of organic and mineral materials. Part of the organic matter associated with the silt fractions shared features in common with POM. In a laboratory bio-assay, biodegradability of POM varied depending on land use (pasture > arable cropping). We concluded that POM is neither homogeneous nor unique.

Botrytis cinerea causes botrytis bunch rot (BBR) disease in wine grapes. Small-scale labour-intensive visual disease assessments may not adequately represent an entire vineyard but larger assessments add cost without necessarily improving accuracy or financial returns. BBR-severity data were collected on three dates from two sites and spatially interpolated. Balanced acceptance sampling (BAS) and simple random sampling (SRS) were compared using sample sizes of 2 to 200 vines. Assessment times were calculated for both walking (rows ignored) and driving (rows impassable) and costs compared with assessment error and effects on crop value. Overall, BAS performed better than SRS. Driving was faster than walking except when sample distribution necessitated travelling down every row regardless of sample size. Annual crop losses of up to NZ$2578 per hectare could result from short assessment times and subsequent inaccurate estimates of BBR severity. Spatial interpolation was shown to be a useful and promising technique for studying BBR sampling strategies in vineyard blocks. Travel was not a substantial component of assessment time. An 80-minute-long assessment could substantially reduce economic losses because of errors in BBR assessments.

A closed incubation assay was conducted to identify attributes that can be used to predict nitrogen (N) supply from dairy cattle effluents applied to contrasting soil types. The experimental design included six slurry (DM 7.9-13%) and six solid (DM 15.9-42.5%) effluents applied to a Horotiu sandy loam or a Templeton silt loam with treatments incubated at 70% of field capacity at 20 degrees C for 2, 5, 14, 21, 35, 42, 63, 96, 119 and 175 days. After 175 days, inorganic N supply (INS) ranged from -38.6 to 82.8% of total effluent N applied while net N mineralisation (NNM) ranged from -107.4 to 65.1% of organic effluent N applied. The best predictor of INS at day 175 was the log ratio of total C to water extractable N (P < 0.001, r = -0.85) and the log ratio of DM to water extractable N (P < 0.001, r = -0.85) (water extractable N = water soluble N + hot water extractable N). These measures were inversely related to INS and effective at explaining both net mineralisation and immobilisation effects. Importantly, correlations between INS and C:N ratio measures were improved considerably when total N was substituted for a labile N measure, in this case water extractable N. We were unable to identify an effluent measure to consistently predict NNM.

Core Ideas SOC decline, due to increased temperatures, reduces wheat and maize yields globally. CO2 increase to 540 ppm partially compensates yield losses due to increased temperatures. Accounting for soil feedbacks is critical when evaluating climate change impacts on crop yield. A critical omission from climate change impact studies on crop yield is the interaction between soil organic carbon (SOC), nitrogen (N) availability, and carbon dioxide (CO2). We used a multimodel ensemble to predict the effects of SOC and N under different scenarios of temperatures and CO2 concentrations on maize (Zea mays L.) and wheat (Triticum aestivum L.) yield in eight sites across the world. We found that including feedbacks from SOC and N losses due to increased temperatures would reduce yields by 13% in wheat and 19% in maize for a 3°C rise temperature with no adaptation practices. These losses correspond to an additional 4.5% (+3°C) when compared to crop yield reductions attributed to temperature increase alone. Future CO2 increase to 540 ppm would partially compensate losses by 80% for both maize and wheat at +3°C, and by 35% for wheat and 20% for maize at +6°C, relative to the baseline CO2 scenario.

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 2 O) 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 N 2 O 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 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland 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 N 2 O emissions. Yield‐scaled N 2 O emissions (N 2 O 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 N 2 O emissions at field scale is discussed.

In modelling the hydrology of Earth's critical zone, there are two major challenges. The first is to understand and model the processes of infiltration, runoff, redistribution and root-water uptake in structured soils that exhibit preferential flows through macropore networks. The other challenge is to parametrise and model the impact of ephemeral hydrophobicity of water-repellent soils. Here we have developed a soil-water model, which is based on physical principles, yet possesses simple functionality to enable easier parameterisation, so as to predict soil-water dynamics in structured soils displaying time-varying degrees of hydrophobicity. Our model, WEIRDO (Water Evapotranspiration Infiltration Redistribution Drainage runOff), has been developed in the APSIM Next Generation platform (Agricultural Production Systems sIMulation). The model operates on an hourly time-step. The repository for this open-source code is https://github.com/APSIMInitiative/ApsimX. We have carried out sensitivity tests to show how WEIRDO predicts infiltration, drainage, redistribution, transpiration and soil-water evaporation for three distinctly different soil textures displaying differing hydraulic properties. These three soils were drawn from the UNSODA (Unsaturated SOil hydraulic Database) soils database of the United States Department of Agriculture (USDA). We show how preferential flow process and hydrophobicity determine the spatio-temporal pattern of soil-water dynamics. Finally, we have validated WEIRDO by comparing its predictions against three years of soil-water content measurements made under an irrigated alfalfa (Medicago sativa L.) trial. The results provide validation of the model's ability to simulate soil-water dynamics in structured soils.

Variable rate nitrogen (VRN) management strategies seek to optimise the nitrogen (N) supply to match crop demand, both to maximise farmer profitability and minimise environmental risks. Despite potential benefits, there has been comparatively little work looking at the value proposition underpinning VRN. Gridded soil samples from a cropping paddock in Hawke’s Bay, New Zealand, were characterised for residual mineral N and a bioassay used to quantify N mineralisation potential. These data were used in APSIM, a systems model, to estimate production outcomes under three different N management strategies in an irrigated and non-irrigated maize cropping system. Predictions showed that yield was comparable between management scenarios, while VRN resulted in lower residual soil N at harvest for both irrigated and non-irrigated systems. While these results are for a single paddock, they demonstrate that in this circumstance the implementation of VRN significantly improved environmental outcomes without impacting gross margins.

C-MIP: An international model inter-comparison simulating organic carbon dynamics in bare fallow soils. 6th International Symposium on Soil Organic Matter

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

The use of dairy effluent to grow forage and arable crops represents an opportunity to more sustainably reuse shed, feed pad and barn nutrients that are generated from intensive dairy systems. To do so in a profitable and low risk manner requires an understanding of the effect of effluent characteristics on nutrient supply patterns, including both the quantum of release and rate of release. Between 2014 and 2016 we have conducted several assays to investigate the nitrogen (N) supplying power of dairy effluents and link this to effluent characteristics measured at the time of application. This paper reports on Assay 1 where we quantified release patterns for five slurry and six solid dairy effluents collected from commercial farms in the Waikato region of New Zealand. These effluents were applied to a single, low N (0.36 % total N) soil at a target application rate of 100 kg N/ha and subsequently incubated in 500 ml units at 20°C and 90% of field capacity for 182 days. Units were leached a total of 15 times during the assay and the drainage water characterised for inorganic N levels. Estimates of N supply were calculated, corrected for background N supply from a noneffluent control, and relationships with a wide range of effluent characteristics assessed. The assay showed that the pattern and magnitude of N supply across slurry and solid effluent treatments varied considerably, consistent with the large variation in effluent characteristics. Strong positive correlations were found between the water-soluble N and carbon (C) effluent characteristics and the rate of N supply in the first month after effluent addition. There were few clear correlations between effluent characteristics and the rate of N supply during the later stages of the assay (112-182 days). At the end of the assay (182 days), final N supply for respective slurry and solid effluents ranged from 3.7 to 74.2 % and 1.5 to 34.3 % of total effluent N applied. Net N supply values which adjusted for inorganic N in the effluents at application (expressed as either a percentage of total N or organic N) were positive for seven of the eleven treatments (three slurries and four solids) indicating a net N mineralisation effect and negative for the remaining four (two slurries and two solids), indicating a net N immobilisation effect. Work is ongoing to identify the causes of the large variation in N supply.