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The determination of optimum nitrogen (N) fertilisation rates, which maximise yields and minimise N losses, remains problematic due to unknown upcoming crop requirements and near-future supply by the soil. Remote sensing can be used for determining the crop N status and to assess the spatial variability within a field or between fields. This can be used to improve N fertilisation, provided that the optimal fertilisation rate at the time of fertiliser application for an expected yield is known. Using the APSIM-wheat model, we developed an algorithm that relates the N status of the plants at early development stages to the yield response to N. Simulations were performed for winter wheat under growth conditions in Denmark. To obtain a range of different N status in the biomass at early growth stages, the soil N in autumn was varied from 20 to 180 kg N ha−1, and at BBCH23, fertiliser was applied at a rate of 50 kg N ha−1. In a full factorial setup, additional N fertiliser was applied ranging from 0 to 150 kg N ha−1 during three different development stages (BBCH30, 32, and 37). The algorithm was evaluated by comparing model outputs with a standard N application of 50 kg N ha−1 at BBCH23 and 150 kg N ha−1 at BBCH30. The evaluation showed that, depending on the N status of the soil, the algorithm either provided higher or lower optimal N fertilisation rates when targeting 95% of the maximum yield, and these affected the grain yield and the grain N, as well as the amount of N leaching. Split application of fertiliser into three applications was generally beneficial, with decreased product-related N leaching of up to nearly 30%. Further testing of the model under different environmental conditions is needed before such an algorithm can be used to guide N fertilisation. [ABSTRACT FROM AUTHOR]

Studies evaluating the complexity of models, which are suitable to simulate grass growth at regional scales in intensive grassland production systems are scarce. Therefore, two different grass growth models (GrasProg1.0 and APSIM) with different complexity and input requirements were compared against long-term observations from variety trials with perennial ryegrass (Lolium perenne) in Germany and Denmark. The trial sites covered a large range of environmental conditions, with annual average temperatures ranging from 5.9 to 10.3 °C, and annual rainfall from 536 to 1154 mm. The sites also varied regarding soil type, which were for modelling categorised into three different groups according to their plant available water (PAW) content: light soils with a PAW of 60 mm, medium soils with a PAW of 80 mm, and heavy soils with a PAW of 100 mm. The objective was to investigate whether the simple model performed equally well with the given low number of inputs, namely climate and PAW group. Evaluation statistics showed that both models provided satisfactory results, with root mean square errors for individual cuts ranging from 0.59 to 1.28 t dry matter ha−1. The model efficiency (Nash–Sutcliffe efficiency) for the separate cuts were also good for both models, with 81% of the sites having a positive Nash–Sutcliffe efficiency value with GrasProg1.0, and 72% with APSIM. These results reveal that without detailed site-specific descriptions, the less complex GrasProg1.0 model can be incorporated into a simple decision support tool for optimising grassland management in intensive livestock production systems. [ABSTRACT FROM AUTHOR]

• Inverse modelling is a valuable tool for the determination of the soil hydraulic conductivity. • Hydraulic conductivities of slowly permeable horizons were determined with APSIM. • Inverse modelling highlighted the importance of the conductivity at field capacity. • Conductivity values were related to morphology characteristic and soil drainage classes. • Water retention parameters predicted by S-map are less important for slowly permeable soils. Soil hydraulic properties play a primary role in water infiltration and movement, and transport of nutrients and contaminants through the soil profile. However, the determination of soil hydraulic properties, especially the hydraulic conductivity at lower depths within the soil profile is very challenging. We investigated the use of inverse modelling based on APSIM (Agricultural Production Simulation Model) to determine likely ranges of the hydraulic conductivity at saturation (K sat) and field capacity (K DUL) for a slowly permeable subsurface horizon. Simulations were run for the poorly drained Otokia silt loam, in the Otago region of New Zealand. APSIM simulations were set up either according to the site specific soil profile description or based on the national soils database (S-map) pedotransfer functions. Simulated temporal soil moisture data were compared to measurements from a field experiment. Within a sensitivity analysis K sat values of the subsurface soil were varied from 2.4 to 192 mm/d, and values for K DUL from 0.05 to 1 mm/d. Finally simulations were run over 45 years using three climatic conditions, with annual rainfall amounts of 582, 628 and 730 mm. The effect of varying the hydraulic conductivity of the subsurface fragipan horizon (C x) on the wetness status of the overlying B 2g horizon was determined by calculating the average annual duration of soil moisture above field capacity. These were then used to estimate ranges of the hydraulic conductivity that contributes to a soil being imperfectly or poorly drained. The results highlight the significant influence that K DUL has on the reliability of modelling the hydraulic behaviour of soils with slowly permeable subsoils. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To investigate the potential use of diverse pasture swards to reduce nitrate leaching from intensive sheep and beef farms while maintaining economic viability, an integrated modelling assessment was conducted for the Canterbury region, New Zealand. The biophysical Agriculture Production Systems Simulator (APSIM) was used to obtain pasture growth curves for simple and diverse pastures over 10 different years, and the whole-farm system models FARMAX® and OVERSEER® were used to examine feed flow, nutrient balance, profitability, and nitrate leaching. The N leaching estimates obtained from OVERSEER® nutrient budget model ( Overseer ) were compared to estimates from APSIM. Five farm scenarios were explored, including three different proportions of the flat farm area under simple and diverse pastures (100% simple, 100% diverse, 50/50), two different stocking policies (without and with adjustment of livestock numbers), and three different years (an average, a best and a worst year, based on annual pasture yields). In the average year pasture growth was similar for the simple and diverse pasture swards, with annual pasture yields of 8.98 and 9.23 t DM/ha. The simple pasture had slightly higher growth in winter due to lower sensitivity to cold temperatures, whereas the diverse pasture showed higher growth during summer, which is frequently prone to water limitations. However, in the best year modelled pasture growth as well as profit were higher for the simple compared with the diverse pasture sward. Pasture N concentrations ranged from 2.5 to 3.5% of dry matter (DM) in the simple pasture, and from 2.2 to 3.1% in the diverse pasture sward, mainly due to a lower proportion of legumes. For the average year, having a diverse pasture on 50% of the farm area without changing the stocking policy of the farm, increased farm profit by 16%, due to the sale of surplus pasture. The total farm N leaching values predicted by APSIM, based on excretal N amounts obtained from Overseer , showed that the use of diverse pastures on 100% of the flat area decreased N leaching in an average year by 35%. In the worst year however, N leaching under the diverse pasture was slightly higher, and in the best year it decreased by 14% compared with the simple pasture. Corresponding reductions in N leaching estimated from Overseer were 6, 5 and 13% for the diverse pasture, primarily due to the lower N concentration of the diverse pasture. In contrast to APSIM, Overseer does not take into account the higher uptake of N of diverse pastures from the urine patches compared to simple pastures, which is mainly due to increased pasture growth in summer of the species included in the diverse pasture. This potential of diverse pastures to decrease N leaching needs to be evaluated via experimental studies, which should also include other aspects such as pasture persistence, and address the need for accurate model parameterisation. The modelling work also provided guidance for model refinement. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Catch crops have been proven effective in reducing nitrogen leaching in cropping systems due to their uptake of post-harvest soil mineral N prior to the main percolation season in winter. Information on the residual effect of catch crops, and their nitrogen fertilizer replacement value is scarce. To determine nitrogen fertilizer replacement values for different catch crops a field experiment was conducted over a four-year period on a sandy soil in Denmark, involving different catch crops and catch crop mixtures with non-legume and legume species. The residual effect of the catch crops was measured in spring barley grown in the residual year. For comparison, winter rye was also included. Over the four-year period the spring barley/fodder radish showed significantly lower leaching compared with the other catch crops, and the winter rye had the highest leaching. Regarding the residual effect, fodder radish and ryegrass, were most valuable, with nitrogen fertilizer replacement values ranging from 13 to 41 kg ha-1, based on fertilizer recovery efficiency of 50%. The process based APSIM (Agricultural Production Systems sIMulator) model was used to simulate the spring barley/catch crop rotations and the winter rye, and showed good model performance regarding N leaching and, crop yield and N uptake. The model was then run over a period of 20 years, to determine variations in N leaching and residual effects due to environmental conditions. The simulations showed that catch crops reduced N leaching by 38–64% when grown every year, and by 21–39% when grown only every second year. Nitrogen fertilizer replacement values ranged on average from 28 to 44 kg N ha-1, but showed high inter-annual variability. The nitrogen fertilizer replacement values showed only a weak relationship with the N uptake of the catch crop prior to termination, likely due to long term effects of N mineralisation from catch crop residues and pre-emptive competition for N by the catch crop. • Catch crops (CCs) were effective at reducing N leaching to or below that of the reduced N fertilizer treatment. • APSIM modelling indicates nitrogen fertilizer replacement values (NFRV) of catch crops ranging from 28 to 44 kg N ha-1. • APSIM modelling indicates that volunteers can be used as a low-cost alternative to CCs. • NFRVs showed high inter-annual variability and were not related to the CCs' N uptake. [ABSTRACT FROM AUTHOR]

Abstract: Nitrogen leaching from urine patches has been identified as a major source of nitrogen loss under intensive grazing dairy farming. Leaching is notoriously variable, influenced by management, soil type, year-to-year variation in climate and timing and rate of urine depositions. To identify early indicators for the risk of N leaching from urine patches for potential usage in a precision management system, we used the simulation model APSIM (Agricultural Production Systems SIMulator) to produce an extensive N leaching dataset for the Waikato region of New Zealand. In total, nearly forty thousand simulation runs with different combinations of soil type and urine deposition times, in 33 different years, were done. The risk forecasting indicators were chosen based on their practicality: being readily measured on farm (soil water content, temperature and pasture growth) or that could be centrally supplied to farms (such as actual and forecast weather data). The thresholds of the early indicators that are used to forecast a period for high risk of N leaching were determined via classification and regression tree analysis. The most informative factors were soil temperature, pasture dry matter production, and average soil water content in the top soil over the two weeks prior to the urine N application event. Rainfall and air temperature for the two weeks following urine deposition were also important to fine-tune the predictions. The identified early indicators were then tested for their potential to predict the risk of N leaching in two typical soils from the Waikato region in New Zealand. The accuracy of the predictions varied with the number of indicators, the soil type and the risk level, and the number of correct predictions ranged from about 45 to over 90%. Further expansion and fine-tuning of the indicators and the development of a practical N risk tool based on these indicators is needed. [Copyright &y& Elsevier]

• Hydraulic conductivity at field capacity (FC) is important for bimodal pore system. • Hydraulic conductivity at FC should be adjusted for soil drainage classes/texture. • Saturated hydraulic conductivity is less important for the temporal soil saturation. • Hydraulic conductivity at FC affects pasture yield. Accurate simulation of soil water dynamics is a key factor when using agricultural models for guiding management decisions. However, the determination of soil hydraulic properties, especially unsaturated hydraulic conductivity, is challenging and measured data are scarce. We investigated the use of APSIM (Agricultural Production Simulation Model) with SWIM3 as the water module, based on Richards equation and a bimodal pore system, to determine likely ranges of the hydraulic conductivity at field capacity (K -10 ; assumed at a matric potential of −10 kPa) for soils representing different drainage characteristics. Hydraulic conductivity measurements of soils with contrasting soil drainage characteristics and values for K -10 were extracted from New Zealand's national soil database. The K -10 values were then varied in a sensitivity analysis from 0.02 to 5 mm d−1 for well-drained soils, from 0.02 to 1 mm d−1 for moderately well-drained soils, and from 0.008 to 0.25 mm d−1 for poorly drained soils. The value of K -10 had a large effect on the time it took for the soil to drain from saturation to field capacity. In contrast, the saturated hydraulic conductivity value had little effect. Simulations were then run over 20 years using two climatic conditions, either a general climate station for all seven different soils, or site-specific climate stations. Two values for K -10 were used, either the APSIM default value, or the soil-specific measured K -10. The monthly average soil saturation level simulated with the latter has a better correspondence with the morphology of the seven soils. Finally, the effect of K -10 on drainage and pasture yield was investigated. Total annual drainage was only slightly affected by the choice of K -10 , but pasture yield varied substantially. [ABSTRACT FROM AUTHOR]

For arable stockless farming systems, the integration of catch crops (CC) during the fallow period might be a key for closing the nitrogen (N) cycle, reducing N leaching and increasing the transfer of N to the subsequent crop. However, despite considerable research efforts, the fate of N in such integrated systems remains unclear. To address this, a two-year field experiment was carried out in northern Germany with different CC, including frost-tolerant and frost-killed CC. The experiment started following a two-year ryegrass/red clover ley, which was subsequently sown with a cereal (CE) or a grain legume (field pea, PE). This provided two contrasting systems with high residual N in autumn. The results showed high N uptake of the CC, ranging from 84 to 136 kg N ha−1 with PE as the pre-crop, and from 33 to 110 kg N ha−1 with CE. All CC reduced N leaching compared with the control, a bare fallow over autumn/winter. Of the various CC, the frost-killed CC showed higher leaching compared with the other CCs, indicating mineralisation of the CC residue in the later autumn/winter period. The process based APSIM (Agricultural Production SIMulator) model was used to simulate N cycling for a cereal grain legume rotation, including a frost-killed and a frost resistant CC. While the model simulated the biomass and the N uptake by the crops, as well as the reduction of N leaching with the use of CC well, it under-estimated N leaching from the frost-killed CC. The study showed that all CC were affective at reducing N leaching, but winter hard catch crops should be preferred, as there is a risk of increased leaching following the mineralisation of residues from frost-killed CC. [ABSTRACT FROM AUTHOR]

• A simple mineralisation model (SMM) based on first order decay and the C:N was tested. • SMM, linked with APSIM could predict N release from catch crop residues well. • The default approach in APSIM underpredicted N mineralisation from catch crops (CC). • APSIM with SMM potential tool for predicting effect of CC on N leaching and N supply. Determining the nitrogen (N) release from catch crop residues is important for N management in arable farming systems. The use of catch crops and subsequent incorporation of its residues represents a nutrient provision for the following crop and has the potential of reducing N leaching. To investigate whether the Agricultural Production Simulator (APSIM) can be used to predict the N release from Brassica catch crop residues with different carbon to nitrogen ratios (C:N) under different temperatures the model was set-up using three different approaches: (i) the generic approach, in which the residue is distributed to the various carbon pools with default APSIM parameters (Gen), (ii) the generic approach but with partitioning of the various pools based on the biochemical composition (Gen-BC), and (iii) the inclusion of an additional carbon pool in which mineralisation is based on first order decay and the C:N of the crop residue, termed simple mineralisation model (SMM). The SMM was parameterized based on results from various previous incubation studies. APSIM was then used to predict N release through mineralisation from these experiments. The simulations indicate that the generic approach in APSIM greatly underestimates the amount of N released from the cover crop residues, especially at low temperatures; the model efficiency (based on the Nash Sutcliffe efficiency score) was negative for the incubation study done at 2 °C. APSIM linked with the SMM shows a substantially higher prediction accuracy. The Gen-BC, which could only be done for one of the incubation experiments, had a higher prediction accuracy than the generic approach, but was not as good as the SMM. This indicates that, for accurate predictions of N mineralisation from Brassica catch crop residues, the generic approach with default partitioning needs to be modified, either by assigning various carbon pools based on the biochemical composition or by including an additional carbon pool. [ABSTRACT FROM AUTHOR]