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«International 17 Workshop th Nitrogen The was jointly organised by Teagasc and AFBI Printed by Print Depot Suggested citation Authors, 2012. Title ...»

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1. Background & Objectives Several N simulation models with different levels of complexity from simple screening tools to research applications have been developed during the last decades. The applicability of a model is related to its complexity. The high data requirements of complex models reduce their applicability to specific conditions. On the contrary, simpler models can be used under wider conditions. For fertilization recommendation purposes the model should be simple enough to make good recommendations adapted to the local fertilizer practices, conditions and information availability. In this sense we planned the objectives of this work to develop a simple N model on a monthly basis (NITIRSOIL) oriented to make fertilizer N recommendations.

2. Materials & Methods The algorithms of NITIRSOIL were developed using Visual Basic.net. The implementation of the algorithms was oriented to be as flexible as possible. A graphical user interface-GUI was designed to allow the non-specialist to use the model. Datasets of soil, climate, and nitrate in irrigation water for all the irrigated areas of the Valencian Community were integrated in the model.

Figure 1. Graphical User Interface (GUI) of the NITIRSOIL model.

The monthly water balance of NITIRSOIL is simulated using the algorithms of the SALTIRSOIL model (Visconti et al., 2011), and the main N balance routines were adapted from the NLEAP model (Shaffer et al., 2010). The crop uptake routine was programmed using the dilution curve concepts (Greenwood et al., 1986) with the following equation.

%N = C1 TDM-C2 The total dry matter (TDM) production was estimated as function of fresh production and the harvest index (HI). The temporal development of dry matter production was assumed to be a Nitrogen Workshop 2012 sigmoidal. The crop parameters (C1, C2) are being calibrated for the main regional crops.

NITIRSOIL assess the NUE index as Nuptake / (Nmin soil at planting + N irrigation + Nfertilizer), to calculate the system N use efficiency.

3. Results & Discussion The crop parameters of NITIRSOIL were calibrated to adjust to the observed N uptake for some vegetables crops in the Valencian Community (potato, onion, artichoke, cauliflower, and lettuce).

Work is in progress to calibrate crop parameters for the main vegetables crops in this region. After this preliminary calibration, the NITIRSOIL was validated for N-NO3 leaching and N crop uptake (Figure 2). Although the observed data were obtained on a daily basis while the model simulates average monthly values, measurements were comparable to predictions. With this validated model, the main terms of the N balance and the mineral N-NO3 in soils can also be predicted. This information can be used to evaluate the impact of N fertilization in crops and the environment and to recommend N fertilization management.

N uptake N leached Predicted (kg N/ha)

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4. Conclusion A new N model NITIRSOIL has been developed. This model was designed to predict the N monthly balance in irrigation soils. The crop parameters of the model were calibrated and the main N balance terms adequately validated for several vegetable crops. The NITIRSOIL provides a tool for farmers to make fertilizer recommendations and also to evaluate the impact on crops and the environment.

References Greenwood, D.J., Neeteson, J.J. and Draycott, A. 1986. Quantitative relationships for the dependence of growth rate of arable crops to their nitrogen content, dry weight and aerial environment. Plant and Soil 91, 281-301.

Shaffer, M.J., Delgado, J.A., Gross, C.M., Follet, R.F. and Gagliardi, P. 2010. Simulation Processes for the Nitrogen Loss and Environmental Assessment Package, In: Delgado, J.A., Follet, R.F., (eds.), Advances for Nitrogen management for water quality, Soil and Water Conservation Society, US.

Visconti, F., de Paz, J.M., Rubio, J.L. and Sanchez, J. 2011. SALTIRSOIL: a simulation model for the mid to long-term prediction of soil salinity in irrigated agriculture. Soil Use and Management 27, 523-537.

Nitrogen Workshop 2012

Strategies to reduce N losses to water from agriculture: experiences from on-farm case studies in the N-TOOLBOX project Cooper, J.M.a, Gascoyne, K.a, Kidd, J.a, Kristensen, H.L.b, Maturano, M.c Quemada, M.c, and van der Burgt, G.J.d a Nafferton Ecological Farming Group, School of Agriculture, Food and Rural Development, Newcastle University, Stocksfield NE43 7XD, UK b Dept.of Food Science, Aarhus University, Kirstinebjergvej 10 DK-5792 Årslev, Denmark c Dpto. Producción Vegetal, ETS Agriculture Engineering, Technical University of Madrid, Avda. Complutense s/n, 28040 Madrid, Spain.

d Louis Bolk Institute, Driebergen, The Netherlands

1. Background & Objectives The movement of nitrates into groundwater and surface water from agricultural sources has been identified as a major environmental and health issue within the European Union. The Nitrates Directive was adopted in December 1991 as a tool to address this issue and the Water Framework Directive was more recently implemented. The N-TOOLBOX project began in 2009 in response to a call from the EU for a project to improve uptake of the Nitrates Directive at the farm level. The project brings together partners in the UK, The Netherlands, Denmark and Spain. Its overall aim is to develop a “toolbox” of cost-effective technologies to be implemented at the farm level to protect water from nitrate pollution. Each project partner is working with farmers to test strategies while noting the techniques that effectively engage farmers in the problem-solving process.





2. Materials & Methods In 2010 and 2011 all four project partners implemented case studies within a selected region of their country, in order to test out the “N-TOOLBOX” approach with farmers. The approaches used in each country varied, and depended on local conditions and knowledge, farmer interests, and the skills and experience of the project scientists. In the Eden Valley region of the UK four participating farmers in 2009 and three in 2010 compared different methods for optimizing fertilizer rate recommendations with their current practice, using test plots of cereals on their farms. In Spain three farms were selected for testing the following strategies: 1) determining optimal fertilizer rates using decision support tools and accounting for soil N supply, 2) replacing intercrop fallow by cover crops, and 3) rotation of crops with high and low N requirements. In The Netherlands case studies were conducted with vegetable farmers on sandy soils, using the NDICEA model (Burgt et al., 2006) to optimize fertilizer rates. In Denmark case studies were performed on three vegetable farms. Reduced rates of nitrogen fertiliser (conventional management) or liquid manure (organic management) based on NDICEA modelling were compared with farmer’s practice, as well as the effect of autumn catch crops compared to winter fallow. At all sites field N dynamics were monitored using a combination of soil, plant and water sampling. Results from some case studies were simulated using the NDICEA model and findings presented to farmers at information meetings.

3. Results & Discussion In the UK no single method for optimizing fertilizer recommendations was best on all farms. There were significant Farm x Treatment effects for grain and straw yields and fertilizer N use efficiency (kg grain kg N fertilizer-1). On one of the study farms, the current farmer practice was already resulting in optimum fertilizer use efficiency and maximum economic returns. In contrast, on another farm, relatively low rates of fertilizer were used and while N use efficiency was relatively

Nitrogen Workshop 2012

high, an economic analysis indicated that profitability could be improved with higher rates of N fertilizer. The case studies served to demonstrate the wide range of approaches for determining fertilizer rates currently used by farmers, and the potential for optimizing rates on many farms.

In Spain adjusting the fertilizer rate to crop N demand based on N supply allowed reductions in N fertilizer without losses in crop yield. In the two farms where the strategy was tested residual N at harvest was reduced. The use of cover crops to take up residual N at the end of the maize growing season greatly reduced nitrate leaching in a wet intercrop period, while it had no effect in years with low winter precipitation. Rotation of summer (high N demand) and winter (low N demand) crops allowed reducing N fertilizer application and leaching risk but decreased farm profitability. The case studies showed that farmers are already adjusting N fertilizer rates to crop needs but there is still a margin for reducing N application by about 20%. Rotation of crops with high and low N requirements is already adopted by farmers to improve water use efficiency, while replacing the intercrop fallow period with a cover crop is not commonly used due to a lack of experience and/or the extra expenses.

In The Netherlands the NDICEA model was useful for demonstrating the impacts of improved practices to farmers. While the NDICEA tool could effectively predict N dynamics on vegetable farms, recommendations were not always taken up. The main reasons for this were: 1) the farmer being unfamiliar with the model, 2) using the surplus of nitrogen as insurance in case of unexpected weather (excess of rainfall), and 3) the farmer relying on his own experience.

In Denmark reduced fertilizer rates did not affect crop yields compared to farmers’ practice and the reduced rates and autumn catch crops decreased the risk of nitrate leaching. Identification of high levels of soil nitrate in spring made farmers realise that reductions in rates of fertiliser are possible without yield reductions. Cooperation was established with the extension service and led to demonstration workshops with advisors and farmers on the use of NDICEA.

4. Conclusion The overall results of the case studies demonstrated that linking scientists with farmers can lead to reduced nitrogen losses by leaching. Scientists gain useful insights into the state-of-the-art currently in use on local farms. Farmers provide candid and direct feedback about results of academic studies. In particular, it was clear that NDICEA is most likely to be useful as a decision support tool when used by trained advisors, rather than by farmers themselves.

The N-TOOLBOX project identified a range of strategies that have been proven to effectively reduce losses of N to water from farms. More direct interactions between farmers, advisors and scientists are now needed to encourage uptake of these measures at the farm level (Barnes et al., 2009). A key factor in this process will be the use of on farm case studies to demonstrate these techniques and empower farmers to make choices about the most appropriate strategy for conditions on their own farms.

References Barnes A.P., Willock J., Hall, C. and Toma L. 2009. Farmer perspectives and practices regarding water pollution control programmes in Scotland. Agricultural Water Management 96, 1715-1722.

Burgt G.J.H.M. van der, Oomen G.J.M., Habets A.S.J. and Rossing W.A.H., 2006. The NDICEA model, a tool to improve nitrogen use efficiency in cropping systems. Nutrient Cycling in Agroecosystems 74, 275-294.

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Knowledge Transfer Poster Presentations Nitrogen Workshop 2012 “Reliquat Virtuel”: a new decision support tool to predict the soil inorganic N pool Damay, N.a, Le Roux, C.a, Gaillard, J.b, Machet, J.M.c, a Laboratoire Départemental d’Analyse et de Recherche de l’Aisne (LDAR), Laon, France b Institut Technique français de la Betterave industrielle (ITB), Paris, France c INRA, Unité Agro-Impact, Laon, France

1. Background & Objectives The main way to calculate fertilizer-N rates to be applied to annual crops is the predictive balance sheet method which is the basis of many decision support tools used by advisors, soil laboratories or farmers. Among these tools, AzoFert® is a software package widely used in northern France since 2005 (Machet et al., 2007). AzoFert® is based on a complete inorganic N balance sheet. It integrates from climatic data a dynamic simulation of soil N supplies and takes into account processes (immobilization and volatilization of ammonia) affecting the availability of fertilizer-N.

Input data include soil type, previous crop, current crop, and farming techniques, easily collected from an information sheet completed by farmers. The annual climate and all necessary data characterizing the different soil types and crops are integrated into the software settings. The soil inorganic N pool at the opening of the balance sheet (usually measured between January and March) is also required as an input. This pool at field scale is either measured at rooting depth or, by default, taken from annual publications. Because of organization, time and costs, all fields of a farm cannot be analyzed.

Another solution is to simulate the soil inorganic N pool at the opening of the balance sheet.

Consequently, a new decision support tool “Reliquat Virtuel” is being developed, using the concepts and algorithms of AzoFert® software adapted to the intercropping period. That new tool could be used to improve accuracy of N recommendation without measurement of the soil inorganic N pool and to guide farmers on the choice of fields to measure.

2. Materials and Methods A first prototype version of “Reliquat Virtuel” has been developed, by adapting the calculation algorithms, parameters and input data of AzoFert® software. The main effort was to take into account the different N fluxes occurring during the intercropping period (from harvest of the previous crop until the opening of the balance sheet). Two modules, one to determine the N mineralization of humified organic matter and the other to determine nitrate transport, were added to the module simulating the contribution of crop residues, catch crops and organic products to N mineralization.



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