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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.

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.

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

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

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

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. We test different methods to simulate yield, water use and soil N in crop rotations.A sensitivity analysis was used to compare multi-year simulations with four crops.Methods combined single/multiple runs of re-initialised/continuous soil conditions.Soil variables were more sensitive to simulation methods than yield variables.Outputs were more sensitive to simulation method for limited crop growth conditions.

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.