A nitrogen balance model for environmental accountability in cropping systems

Cichota, Rogerio; Brown, Hamish E.; Snow, Val; Wheeler, D. M.; Hedderley, Duncan; Zyskowski, R. F.; Thomas, S.

doi:10.1080/01140671.2010.498401

Cited by 9 publications

(7 citation statements)

References 25 publications

(20 reference statements)

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“…The Background N sub-model is based on N received from non-urine inputs in surface applied, plant and soil sources (Cichota et al 2010). Sources are split between slow release (organic N) and quick release (mineral N) pools.…”

Section: Fate Of Nitrogen In a Pastoral Blockmentioning

confidence: 99%

Understanding the distribution and fate of nitrogen and phosphorus in OVERSEER®

Selbie¹,

Watkins²,

Wheeler³

et al. 2013

ProNZG

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OVERSEER® Nutrient Budgets (Overseer) is an agricultural management support tool that examines the flow of nutrients in a farming system. There is increasing pressure from a range of users for transparency of the way Overseer functions, particularly the modelling of nitrogen (N) and phosphorus (P) loss to water. The aim of this paper is to provide a conceptual description of the way Overseer models the distribution and fate of N and P in a pastoral system, to support user understanding and correct model use. The core of Overseer is a nutrient budget, which accounts for the flow of nutrient into, around and off the farm. The key strength of Overseer is its ability to model these nutrient transfers around the farm, identifying how much, where and when nutrients move. Other parts of the model then estimate the fate of these nutrients. Nitrogen and P cycle differently around the farm, which is reflected in the way they are modelled. This paper is intended to be a support document for understanding the way Overseer models N and P, and where more detailed information is required, it may be found on the Overseer website (www.overseer.org.nz). Keywords: Nutrient budget, model, leaching, run-off

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Section: Fate Of Nitrogen In a Pastoral Blockmentioning

confidence: 99%

Understanding the distribution and fate of nitrogen and phosphorus in OVERSEER®

Selbie¹,

Watkins²,

Wheeler³

et al. 2013

ProNZG

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“…For Layer 1, eight key generic soil horizons (Allophanic Silt Clay Loam, Clay Loam, Silt, Silt Loam, Sandy Loam, Loamy Sand, Sand and Gravelly Sandy) were used, whereas for Layer 2 only three horizons (Allophanic Silt Clay Loam, Silt, and Sand), and for Layer 3 only two selected horizons (Clay Loam and Gravelly Sandy) were used. As previous simulations have indicated (Cichota et al, 2010a) that the risk of N leaching is highly dependent on the plant available water within the rootzone (PAW) we categorized the soils into five different soil class depths: PAW < 100 mm very shallow, PAW of 100-125 shallow, PAW 125-150 medium, PAW 150-175 deep, and PAW > 175 very deep. For the evaluation of the N risk tool independent data was required.…”

Section: Soil Typesmentioning

confidence: 99%

Development and desktop-assessment of a concept to forecast and mitigate N leaching from dairy farms

Vogeler

Cichota²,

Jolly³

2011

Chan, F., Marinova, D. And Anderssen, R.S. (Eds) MODSIM2011, 19th International Congress on Modelling and Simulation.

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Pastoral systems are increasingly under scrutiny for the nitrogen (N) deposited via urine, faeces and fertiliser that is subsequently transported through the soil profile into water ways. Variation in land management, fertiliser N application rates, soil type, climate and year-to-year variation in weather all affect the rate and timing of N losses via leaching. To mitigate N leaching, a better understanding of the environmental conditions and the identification of early indicators that lead to high risk of N leaching are needed. To identify early indicators for risk of N leaching from urine patches we used the APSIM (Agricultural Production Systems SIMulator) model to produce an extensive N leaching dataset for the Waikato region of New Zealand. In total 198000 simulation runs with different combinations of urine deposition date, N loading, soil type and irrigation were done. The risk forecasting indicators were chosen based on their practicality: being easy to be measured on farm (soil water content, temperature and pasture growth) or that could be centrally measured (such as weather forecasting). The thresholds of the early indicators that are likely to forecast a period for high risk of N leaching were determined via classification and regression tree (CART) analysis. This concept is illustrated in Figure 1 for a soil with a medium soil depth, with soil plant available water of 125-150 mm. The identified early indicators were then used within a N risk tool, based on a traffic light system, to trigger duration controlled grazing in a desktop evaluation to reduce N leaching on a paddock scale. Predicted reductions in N leaching ranged from 20 to 45%, depending on the number of indicators used and the risk alertness of the farmer.

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“…Field studies designed to provide this information are both time consuming and costly and cannot cover all scenarios. Simulation models are viable alternatives that have been successfully applied to analyse management scenarios and how they affect N loss in the field 4–6 . Scenario analyses allow faster and thorough testing of innovative management options before they are implemented.…”

Section: Introductionmentioning

confidence: 99%

Simulation of management strategies to mitigate nitrogen losses from crop rotations in Southland, New Zealand

2021

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BACKGROUND Nitrogen (N) fertiliser used on crops is among the main sources of water pollution. Reliable measurement of N losses from land uses in catchments is key to designing effective management strategies that minimise those losses at the same time as keeping farms profitable. In the present study, we used a management simulation tool within the Agricultural Production Systems sIMulator (APSIM) to assess the effect of fertiliser management on N leaching from croplands in the Aparima catchment in Southland, New Zealand. The assessment was based on two N‐fertiliser regimes: (i) Scheduled (conventional) where, N‐fertiliser rates and timing of application followed a prescribed programme, and (ii) Soil‐test where, N‐fertiliser rates and timing depended on daily analysis of simulated soil N levels. Four rotations (continuous wheat, pasture‐wheat‐grain oats, wheat‐fodder beet‐peas and wheat‐green oats‐fodder beet‐peas) were used in the evaluation. RESULTS APSIM simulated crop productivity with reasonable accuracy. Yields were 2% greater, fertiliser N input was 11% lower and leaching was 20% lower under the Soil‐test compared to the Scheduled fertiliser management. These results show the potential of a Soil‐test based fertiliser application to increase fertiliser‐N use efficiency and reduce the risk of N loss to the Southland catchment water systems. CONCLUSION The present study demonstrates a dynamic farm systems model can be a viable tool to generate valuable data for assessing the productivity and environmental effects of cropping systems at a catchment scale. © 2021 Society of Chemical Industry

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A nitrogen balance model for environmental accountability in cropping systems

Cited by 9 publications

References 25 publications

Understanding the distribution and fate of nitrogen and phosphorus in OVERSEER®

Understanding the distribution and fate of nitrogen and phosphorus in OVERSEER®

Development and desktop-assessment of a concept to forecast and mitigate N leaching from dairy farms

Simulation of management strategies to mitigate nitrogen losses from crop rotations in Southland, New Zealand

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