Rogerio Cichota

Estimating nutrient loss to waterways - an overview of models of relevance to New Zealand pastoral farms.

Abstract Reliable estimation of nutrient losses from farmland is of increasing interest, driven by both economic and environmental concerns. Routine direct measurement of nutrient losses is currently impractical given the scale and variability of the problem. Simulation models are the best alternative and their use for assessing potential nutrient losses has been increasing worldwide. In New Zealand, there are a considerable number of models in use, or that are being developed, aiming to estimate N and P losses from pastoral fields. This range of alternative models reflects both the different level of detail and scale at which N and P losses can be estimated, and the diverse range of purposes assumed during the model development. Thus, it is important to understand the differences between models in order to select the one that will produce estimates appropriate to the intended use. This work presents an overview of the principal models for estimating nutrient loss being used or developed in New Zealand. It emphasises models that deal with N and P losses from pastoral farming systems, particularly via leaching, and that may allow the handling of different farm management procedures. Most of the models have gone through some testing and are supported by published works, although some are not fully operational yet and others need further evaluation. There is, in general, a lack of organised information about how several of these models work and what their main purposes are. We aim to supply some basic information about the available tools, sorting them into categories to highlight their primary differences and similarities. This is intended to assist discussions about model selection as well to highlight where information gaps about particular models need to be addressed.

Describing N leaching from urine patches deposited at different times of the year with a transfer function

A transfer function (TF) was developed to assist with the estimation of nitrogen (N) leaching from urine-affected areas in grazed pastures. The proposed TF uses a simple function to describe the likely breakthrough curve for urine-N deposited in different months and in various climates and soils in New Zealand. The TF was designed to be integrated into the OVERSEER® Nutrient budgets model to increase the sensitivity of N leaching to the month of urine deposition, but could also be used in any other model that estimates the water balance and plant N uptake on a monthly basis. The inputs required for the TF are typically readily accessible (e.g. soil texture data) and thus do not add any significant complications when added to OVERSEER. The TF retains OVERSEER as the arbitrator of the main items of N-balance in the farm system, but adds functionality by giving a better temporal discrimination of leaching from the farm system. The procedure for parameterising the TF from a comprehensive set of APSIM (Agricultural Production Systems Simulator) simulations is described. Validation of the leaching estimated by the TF was achieved through a combination of testing against an independent set of APSIM simulations and testing against experimental data. The testing of the TF showed very promising performance. The TF explained 75% of the variability of N leaching simulated by an independent APSIM dataset and agreed well with the experimental data.

Ensemble pedotransfer functions to derive hydraulic properties for New Zealand soils

Modelling water and solute transport through soil requires the characterisation of the soil hydraulic functions; however, determining these functions based on measurements is time-consuming and costly. Pedotransfer functions (PTFs), which make use of easily measurable soil properties to predict the hydraulic functions, have been proposed as an alternative to measurements. The better known and more widely used PTFs were developed in the USA or Europe, where large datasets exist. No specific PTFs have been published for New Zealand soils. To address this gap, we evaluated a range of published PTFs against an available dataset comprising a range of different soils from New Zealand and selected the best PTFs to construct an ensemble PTF (ePTF). Assessment (and adjustment when required) of published PTFs was done by comparing measurements and estimates of soil water content and the hydraulic conductivity at selected matric suction values. For each point, the best two or three PTFs were chosen to compose the ePTF, with correcting constants if needed. The outputs of the ePTF are the hydraulic properties at selected matric suctions, akin to obtaining measurements, thus allowing the fit of different equations as well as combining any available measurements. Testing of the ePTF showed promising performance, with reasonably accurate estimates of the water retention of an independent dataset. Root mean square error values averaged 0.06 m3 m–3 for various New Zealand soils, which is within the accuracy level of published PTF studies. The largest errors were found for soils with high clay content, for which the ePTF should be used with care. The performance of the ePTF for estimating soil hydraulic conductivity was not as reliable as for water content, exhibiting large scatter. Predictions of saturated hydraulic conductivity were of the same magnitude as the measurements, whereas the unsaturated values were generally under-predicted. The conductivity data available for this study were limited and highly variable. The estimates for hydraulic conductivity should therefore be used with much care, and future research should address measurements and analysis to improve the predictions. The ePTF was also used to parameterise the SWIM soil module for use in Agricultural Production Systems Simulator (APSIM) simulations. Comparisons of drainage predicted by APSIM against results from lysimeter experiments suggest that the use of the derived ePTF is suited for the estimation of soil parameters for use in modelling. The ePTF is not envisaged as a substitute for measurements but is a useful tool to complement datasets with limited amounts of measured data.

A functional evaluation of virtual climate station rainfall data

Abstract Model simulations are increasingly being used to assess farm performance and its impact on the environment. A crucial input in most of these models is daily rainfall data, which are difficult to obtain for remote areas or if the period required is long. Recently, a new long‐term daily climate dataset for all New Zealand, known as Virtual Climate Station (VCS) data, has become available from NIWA (National Institute of Water & Atmospheric Research Ltd). A reasonable agreement between the VCS and observed data has been reported; nonetheless the effect of the uncertainty in the estimated rainfall on model simulations remains an issue to be examined. In this study we investigated the differences in simulated evapotranspiration, drainage and pasture growth by two models of varying complexity when using VCS or observed rainfall data from several locations around New Zealand. The results showed a general agreement between model outputs whether using observed or VCS rainfall dataseis. Model outputs that are closely related to rainfall events, such as drainage, were more sensitive to deviations in the rainfall dataseis than the indirect outputs, such as evapotranspiration. Data aggregation and correction of bias improved agreement between observed and VCS data and this was reflected in the model outputs. Although short‐term simulations should be analysed with care, overall results were promising. The NIWA VCS rainfall data have the potential to be extensively used in conjunction with simulation models for the assessment of land use and management practices.

Soil assessment of apple orchards under conventional and organic management

To determine the effect of wheel traffic and two different management practices on soil compaction and its consequences on physical and chemical soil properties, we measured penetration resistance, water infiltration, bulk density, macroporosity, chemical mobility, air permeability, and soil strength in a conventional orchard (integrated fruit-production program) with bare (sprayed with herbicides) rows and an organic apple orchard with grassed rows. Resistance measurements were taken both within the tree row and the wheel track, down to a depth of 0.35 to 0.40 m. The results indicate that compaction is greater in the wheel tracks under both management methods. Compaction in the wheel track was higher under organic than conventional management. Organic management resulted in a higher macroporosity in both the row and the wheel-track than conventional management. The ‘close-to-saturation’ infiltration rate was significantly greater within the row of the organic orchard (0.06 m/h) compared with the row of the conventional orchard (0.02 m/h), and compared with the wheel tracks (0.01 m/h). The precompression stress value in the top 100 mm, a measure of the soil strength, was low on all sites. The chemical mobilities were 57 and 50% in the organic orchard, and 86 and 93% in the conventional orchard, respectively, for wheel track and row. Apart from the compaction in the wheel track of the organic orchard, physical and chemical soil characteristics were in a better condition compared with the conventional orchard.

Comparison of APSIM and DNDC simulations of nitrogen transformations and N2O emissions

Various models have been developed to better understand nitrogen (N) cycling in soils, which is governed by a complex interaction of physical, chemical and biological factors. Two process-based models, the Agricultural Production Systems sIMulator (APSIM) and DeNitrification DeComposition (DNDC), were used to simulate nitrification, denitrification and nitrous oxide (N2O) emissions from soils following N input from either fertiliser or excreta deposition. The effect of environmental conditions on N transformations as simulated by the two different models was compared. Temperature had a larger effect in APSIM on nitrification, whereas in DNDC, water content produced a larger response. In contrast, simulated denitrification showed a larger response to temperature and also organic carbon content in DNDC. And while denitrification in DNDC is triggered by rainfall ≥5mm/h, in APSIM, the driving factor is soil water content, with a trigger point at water content at field capacity. The two models also showed different responses to N load, with nearly linearly increasing N2O emission rates with N load simulated by DNDC, and a lower rate by APSIM. Increasing rainfall intensity decreased APSIM-simulated N2O emissions but increased those simulated by DNDC.

Comparison between APSIM and NZ-DNDC models when describing N-dynamics under urine patches

Nitrous oxide (N2O) emissions from soil are the result of complex interactions between physical, chemical and biological processes. We compared two process-based models (APSIM and NZ-DNDC) with measurements of N2O emissions, soil and content (0–75 mm) and water-filled pore space from a series of field campaigns where known amounts of animal urine-N were applied to four soil types under permanent pastures, in two regions within New Zealand, at different times of the year. We also compared cumulative N2O emissions with an N2O inventory emission factor approach (EF3 method). Overall, the two process-based models performed less well than the EF3 method for simulating cumulative N2O emissions over the complete data set. However, in winter, the APSIM model correlated well with measurements (r = 0.97), while NZ-DNDC performed well on the Otago soils (r = 0.83 and 0.92 for Wingatui and Otokia, respectively). The process-based models have the potential to account for the effect of weather conditions and soil type on N2O emissions that are not accounted for by the EF3 method. However, further improvements are currently needed. The fractions of N lost to different processes within the complex soil–plant atmosphere system differed between the two models. The size of the predicted plant uptake, leaching and NH3 volatilisation fluxes are large compared with N2O emissions and could affect the simulated soil N pools and thus the predicted N2O fluxes. To simulate N2O fluxes accurately, it is therefore important to ensure these processes are well modelled and further validation studies are needed.

Increasing the spatial scale of process-based agricultural systems models by representing heterogeneity

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.

Effects of irrigation intensity on preferential solute transport in a stony soil

ABSTRACT If irrigation intensity exceeds soil infiltration capacity, water may flow preferentially down cracks and large pores. In this situation, solute transport will involve only a fraction () of the soil’s water and leaching rate may be affected. To assess whether irrigation intensity affects preferential solute flow, an experiment was performed at Lincoln using 12 steel-encased lysimeters with a Lismore Stony Silt Loam soil under two irrigation intensities, 5 and 20 mm h–1. Burns’ equation was used to describe the measurements of non-reactive tracer concentration as a function of drainage. Under dry antecedent moisture conditions, bromide transport was not significantly different under the different irrigation rates, even though strong preferential leaching occurred, with of 0.23. For chloride, was 0.85 and 0.58, for 5 and 20 mm h–1 respectively, sufficient evidence to confirm the effect of irrigation intensity (P < 0.05). By assuming to be 1.00 for the median rainfall at Lincoln, an exponential function was fitted to the data, suggesting a lower limit of 0.35 for under moist conditions. Implications for nutrient leaching are discussed.

Identification and testing of early indicators for N leaching from urine patches

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.