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1. Background & Objectives Enhancement of nitrogen use efficiency (NUE) is an important prerequisite in development of sustainable cropping systems. Production of crops, such as potato, that have a short period of Nuptake and a shallow rooting system is particularly influenced by low NUE. Several optically based and ground-based methods have been developed as tools to assess potato crop nitrogen status (CNS) (Hatfield et al., 2008) in order to improve the decision on the need for complementary N application (Goffart et al., 2008) and consequently NUE. The objective of this study was to explore the potential (sensitivity, specificity, and accuracy/precision) of large scale satellite multi-spectral (MS) image data for assessing CNS of potato compared to non-invasive ground-based data on leaf chlorophyll content obtained with a handheld chlorophyll meter (CM). This abstract presents the results of two years of trials conducted in Belgium, in potato field plots of variable sizes receiving a range of fertilizer N rates. Remotely sensed data from SPOT-5 satellite MS imagery (off-nadir) were also compared with handheld radiometer ground-based reflectance data (nadir view) for validation through several wavebands vegetation indices (VI) calculated according to literature references.
2. Materials & Methods The study was conducted on 11 and 9 commercial potato crops in 2008 and 2009, respectively. In each field, plots of variable sizes received increasing fertilizer N rates (zero-N as reference plot, 70% and 100% of recommended N rate, except for 7 fields in 2008 that received only 100% N) were used to validate satellite images. Ground-based optical data with a CM SPAD/HNT (Yara, Oslo, Norway) and radiometer Cropscan (Cropscan, Rochester, USA) were obtained at intervals of 7-10 days from 20-75 DAE. Spot-5 satellite MS images were acquired on July 25, 2008 and August 5, 2009 when the potato crop was at the end of its vegetative stage between 55 and 75 days after emergence (DAE). The number of useful pixels of the MS images considered in each N plot varied from one pixel for the smallest plot in 2009 to about 30 pixels for the larger ones in 2008. Pixel digital number (DN) were extracted for each of three MS wavebands (green (G), red (R) and near infra-red (NIR)) using the ENVI image analysis software (version 4.3). To compare the satellite MS data with ground-based MS radiometer Cropscan (Cropscan, Rochester, USA) data, Top-of-Canopy (TOC) reflectance values were calculated from DN data extracted from the SPOT-5 satellite image in 2008. Different VI’s were calculated from either satellite DN or ground-based radiometer data, and assessed for their sensitivity and accuracy/precision to discriminate between the different N plots, and for their specificity to CNS through their relationship to the biomass N content or Nuptake assessment in each N plot.
3. Results & Discussion The comparison of TOC satellite data with ground-based reflectance measurements showed high coefficients of determination which is a good validation of the SPOT-5 data. Satellite data showed high sensitivity in discriminating the different N rates across a whole canopy, even taking into account a limited number of pixels. Significant discrimination was particularly obvious between the
4. Conclusion This study indicates the potential of SPOT-5 (spatial and spectral resolution) to monitor potato CNS. The DN or derived VI data were accurate and showed high sensitivity to CNS even for small plot areas within a field. Operational use of such data acquisition in management strategy or decision support system for potato N fertilization at regional scale still needs to be explored further.
Results should be confirmed with earlier acquisition dates within potato growing season.
References Bausch, W.C. and Khosla, R. 2010. QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture 11, 274-290.
Goffart, J.P., Olivier M. and Frankinet M. 2008. Potato crop nitrogen status assessment to improve N fertilization management and efficiency : Past-Present-Future. Potato Research 51,355-383.
Hatfield, J.L.,Gitelson A.A., Schepers J.S. and Walthall C.L. 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100, S117-S131.
Olivier, M., Goffart J.P., Ledent J.F. 2006. Threshold values for chlorophyll meter as decision tool for nitrogen management of potato. Agronomy Journal 98, 496-506.
Nitrogen Workshop 2012
Sharing scientists’ and stakeholders’ knowledge in a DSS to reduce nitrogen losses in cropping systems V. Parnaudeaua, R. Reaua, P. Dubrullea, C. Aubertb, A. Bailletc, N. Beaudoina, P. Béguina*, F.
Butlerf, P. Cannavoa,j, J.-P. Cohand, A. Duponta, R. Duvale, S. Espagnoli, J.P. Fagnieza, F. Flénetc, L. Fourriéf, S. Génermonta, L. Guicharda, M.-H. Jeuffroya, E. Justesa, F. Laurentd, J.-M. Macheta, F.
Maupase, T. Morvana, S. Pellerina, C. Raisong, C. Raynalh, S. Recousa, J. Thiarda a INRA, Département Environnement et Agronomie, * Département SAD, France;
b ITAVI, France; c CETIOM, France; d ARVALIS-Institut du végétal, France; e ITB, France; f ACTA, France;
g Institut de l’élevage, France; hCTIFL, France ; i IFIP, France; j Agrocampus-Ouest, France
1. Background & Objectives Implementation of European Union (EU) Water Framework Directive (WFD, and more generally development of sustainable agriculture, require to reduce N losses (under gaseous or nitrate forms) and avoid pollution swapping in cropping systems. This relies on production system diagnosis and on design of innovative systems. In this context, one of the acute issues remains the improvement of nitrogen management, based on assessment and diagnosis of nitrogen use by the plant, N losses and impacts in agricultural systems. A Decision Support System (DSS), called Syst’N, has been developed, by sharing knowledge on N fluxes in agricultural systems between agricultural scientists and stakeholders. This DSS includes a dynamic nitrogen model to calculate N losses and a database gathering N loss results in different contexts; it is mainly intended for use by environmental managers and agricultural extension services.
2. Materials & Methods The DSS requirements were determined from results of a survey (Parnaudeau at al. 2007).
Following these specifications, different prototypes of the DSS interfaces were proposed and discussed between the designers, and finally proposed to a panel of potential users for their comments and remarks to improve our tool. This step in the concept required an inter-disciplinary approach combining ergonomic and agronomic sciences to organize our experimental design. In order to estimate, to understand, and to explain N losses as well as to diagnose N nutrition problems at the cropping system scale, it was necessary to describe the complete management of each crop in the rotation, and the soil and climate characteristics. Development of the DSS also included the choice and development of the dynamic N model. An exhaustive bibliographical analysis was performed to establish the state of the art concerning both N models and tools like DSS or indices to diagnose N losses (Cannavo et al., 2008). On this basis, we have integrated existing sub-models from the literature for our specific purpose, i.e. valid for a large range of agricultural, soil and climatic conditions. The selected sub-models were also chosen because of their reliability when used with available data from target users. Those choices involved several discussions between modellers, but the panel of potential users was not directly consulted for this modelling stage.
3. Results & Discussion The interface for data entry includes default data, and enables the comparison of various cropping systems or the consideration of climatic uncertainties. In order to help users, default input databases propose the description of regional soils (three of the French ones in the prototype) and cropping systems. Each simulation folder describes the cropping system within its context, through a tree structure representation. The simulator calculates the different N losses: NO3- leaching, NH3 volatilisation and N2O losses by denitrification, especially to be able to evaluate pollution swapping when changing cultural techniques or cropping systems. The model runs in daily time steps, in
Nitrogen Workshop 2012
order to take account of gaseous losses as volatilisation. It integrates existing sub-models: soil organic matter and crop residue mineralisation is predicted by AZOFERT (Machet et al., 2004), denitrification is predicted by NOE model (Hénault et al., 2005), water balance and nitrate leaching are based on STICS model (Brisson et al., 1998, Brisson et al., 2009), and N uptake is based on AZODYN model (Jeuffroy et al., 1999). Some sub-models, i.e. manure mineralisation and volatilisation modules, were simplified by determining statistical relationships using a large dataset instead of developing mechanistic equations, to better take into account local soil and climatic conditions. The functional prototype of the software has to be tested and validated during the current year with external datasets. For crops, anticipated yield will likely need to be included as an input to improve prediction of crop growth and N uptake, in order to improve soil mineral N pool calculation in autumn and consequently N leaching (Makowski et al., 2009). Another step is the adaptation of the N model to cropping systems including grasslands or vegetable crops in order to make the tool more generic so that its use can be extended to various regions.
4. Conclusion Simulation models need to be adapted to be useful to stakeholders as shown by Cox (1996) and MacCown (2002a, 2002b). We have built a DSS combining a user interface with a dynamic model, and proposed default data. We are going to continue designing and assessing the tool, by involving stakeholders in the improvement of the DSS through a learning loop. On this basis, we will develop a learning activity with stakeholders in order to improve assessment of N losses and to enable the use of simulation and virtual experimentation.
References Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M.-H., Ruget, F., Nicoullaud, B., Gate, P., Devienne-Barret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J.M., Meynard, J.M. and Delecolle, R. 1998. STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18, 311-346.
Brisson N., Launay M., Mary B. and Beaudoin N. 2008. Conceptual basis, formalisations and parameterization of the STICS crop model. Editions Quæ, Paris, France.
Cannavo, P., Recous, S., Parnaudeau, V. and Reau, R. 2008. Modeling N dynamics to assess environmental impacts of cropped soils. Advances in Agronomy 97, 131-174.
Cox P.G. 1996. Some issues in the design of agricultural decision support systems. Agricultural systems 52, 355Hénault C., Bizouard F., Laville P., Gabrielle B., Nicoullaud B., Germon J.C. and Cellier P. 2005. Predicting in situ soil N2O emission using NOE algorithm and soil database. Global Change Biology 11, 115-127.
Jeuffroy M.H. and Recous S. 1999. Azodyn: a simple model simulating the date of nitrogen deficiency for decision support in wheat fertilization. European Journal of Agronomy 10, 129-144.
Machet, J.M., Recous, S., Jeuffroy, M.H., Mary, B., Nicolardot, B. and Parnaudeau, V. 2004. A dynamic version of the predictive balance sheet method for fertiliser N advice. Controlling Nitrogen Flos and Losses, Inst Grassland & Environm Res, 191-193.
Makowski, D., Tichit, M., Guichard, L., Van Keulen, H. and Beaudoin, N. 2009. Measuring the accuracy of agroenvironmental indicators. Journal of Environmental Management 90, Supplement 2, 139-146.
McCown, R.L. 2002a. Locating agricultural decision support systems in the troubled past and socio-technical complexity of ‘models for management. Agricultural systems 74, 179-220.
McCown, R.L. 2002b. Changing systems for supporting farmers’ decisions: problems, paradigms, and prospects.
Agricultural systems 74, 179-220.
Parnaudeau, V., Reau, R., Duval, R., Fourrié, L., Gillet, J.P., Guichard, L., Justes, E., Laurent, F., Machet, J.M., Maupas, F., Morvan, T. and Raynal, C. 2007. A sociological approach to determine the advisers and stakeholders requirements for nitrogen management and diagnosis tools. 15th European N workshop, Lleida.
Nitrogen Workshop 2012
Soil quality as affected by organic and mineral N fertilization in maize Biau, A.a, Mijangos, I.b, Lloveras, J.c a University of Lleida (UdL), Av. Rovira Roure 191, Lleida 25198, Spain.
b Department of Ecosystems, NEIKER-Tecnalia, Basque Institute of Agricultural Research and Development, c/Berreaga 1, E-48160 Derio, Spain.
c Centre UdL-IRTA, University of Lleida (UdL), Av. Rovira Roure 191, Lleida 25198, Spain.
1. Background & Objectives Pig slurry is considered an important potential source of nitrogen (N) in the Ebro Valley areas (Daudén and Quílez, 2004), where soils are often relatively low in organic matter (OM) (frequently 2%) (Ferrer and Sanz, 1983). Manure applications may help to enhance soil fertility, to maintain soil quality (Brechin and McDonald, 1994) and to improve soil structure with the increase of OM (Berenguer et al., 2008). The objective of this study was to evaluate the effect of long term organic and mineral N fertilization on selected soil quality indicators in continuous maize under irrigation.
2. Materials & Methods Field experiments were conducted from 2002 to 2010 under sprinkler irrigation.
Fertilization treatments consisted on pig slurry (PS) application and a mineral N fertilization applied as ammonium nitrate (33.5%) as a sidedress. A zero N rate was also studied as a control (N0). Annual PS application rate was about 50 m3 ha-1 (PS50) (290 kg N ha-1) and mineral N fertilization rate was 300 kg N ha-1 (N300). Grain yield, biomass at maturity, plant N uptake and soil NO3--N were measured before and after harvest. Earthworms’ abundance, soil compaction and OM were used as soil quality parameters.
3. Results & Discussion Average grain yield from 2002 to 2010 responded differently (p0.05) to the source of N (Table 1). Nitrogen fertilization at 300 kg N ha-1 had the highest grain yield. We think that this result was mainly due to the N volatilization losses of the PS50 (20% approximately) during the spreading of the slurry and also to the different N application times. The PS was applied before planting, whereas the N300 was applied as a sidedress. It is known that the sidedress N is more efficient than the pre-planting applications. Furthermore, part of the N applied with PS was possibly immobilized in the soil as organic forms, and can be released over a period of many years (Schröder, 2005).
Biomass production did not show differences between treatments, possibly because the amount of N in the soil was enough (Table 1). Nitrogen uptake significantly increased with the application of N fertilization compared with N0 over the years (Table 1). Our results were in line with those reported by Andrade et al. (1996) whose plant N content ranged from 240-300 kg N ha-1. Residual soil NO3--N content at the end of the trail was higher with mineral than organic fertilization (Table 1), probably, as it was said before, because the higher amount of N really applied, and the higher N efficiency of the N300 treatment.
Pig slurry applications produced higher soil quality values, compared to N300 and N0 (Table 2). So, the results seem to indicate that long term organic fertilization improves soil quality in our conditions.
Table 1. Effect of N fertilization rates on grain yield, biomass at maturity, plant N uptake, soil NO3--N content before planting (initial) and after harvest (residual), from 2002 to 2010 in Gimenells.
Pig slurry applications increased the soil OM content in the experiment, as it was shown by Berenguer et al. (2008), and consequently it can affect soil compaction (Table 2).
Earthworm abundance presented higher values in PS than in the other N fertilization treatments (Table 2). As Pankhurst et al. (1997) reported earthworms’ abundance could provide a more sensitive bioindicator of soil quality than physic-chemical parameters such as OM content.
Table 2. Effect of N fertilization rate on physical, chemical and biological soil quality indicators in 2010 in Gimenells.
4. Conclusion Long term organic fertilization with PS shows a slight improvement in soil compaction and OM. Furthermore, earthworms’ abundance was clearly different among treatments and it can contribute to soil quality improvement under irrigated conditions. Therefore, we interpret PS applications as a good management procedure.
References Andrade, F.H., H.E. Echeverría, N.S. González and Uhart, S.A. and Darwich, N.A. 1996. Requerimientos de nitrógeno y fósforo de los cultivos de maíz, girasol y soja. EEA INTA Balcarce. Buenos Aires. Boletín Técnico 134, 1-17.
Berenguer, P., S. Cela, F. Santiveri, J. Boixadera and J. Lloveras. 2008. Copper and zinc soil accumulation and plant concentration in irrigated maize fertilized with liquid swine manure. Agron. J.
Brechin, J. and G.K. McDonald. 1994. Effect of form and rate of pig manure on the growth, nutrient uptake and yield of barley (cv. galleon). Aust. J. Exp. Agric. 34, 505-510.
Daudén, A. and D. Quílez. 2004. Pig slurry versus mineral fertilization on corn yield and nitrate leaching in a mediterranean irrigated environment. Eur. J. Agron. 21, 7-19.
Ferrer, P.J. and J.B. Sanz. 1983. Posibilidades de utilización agrícola del estiércol liquido porcino (ELP) en relación a su valor fertilizante su incidencia sobre el suelo. composición y valor fertilizante del ELP.
Anales INIA, Serie Agrícola 23:35-57.
Pankhurst, C., B. Doube and V. Gupta. 1997. Biological indicators of soil health. CAB INTERNATIONAL,.
Schröder, J. 2005. Revisiting the agronomic benefits of manure: A correct assessment and exploitation of its fertilizer value spares the environment. Bioresour. Technol. 96, 253-261.
Nitrogen Workshop 2012
The rice crop response to pig slurry fertilization in Ebro Delta area (Catalonia, Spain): four seasons studied (2008-2011).
Català, M.M.a, Pla, E.a, Martínez, M.a,b, Tomàs, N.a a IRTA. Research & Technology Food & agriculture. Amposta. Ctra. Balada, km 1. 43870. Amposta. Spain b Present address: Department of Agriculture, University of Reading, Earley Gate, Reading RG6 6AR, UK
1. Background & Objectives Catalonia is the first Spanish region in the national register of pigs (26.3% of the national register) (MARM, 2010) with a census higher than 25 millions. The use of pig slurry (PS) as fertilizer is the most common recycling methodology.
The world energetic crisis and the increasing costs of chemical fertilizer have encouraged the interest for the use of farmyard manure (Meelu and Morris, 1987) which can guarantee a larger amount of macro and micronutrients (Mn, Cu, Zn and Fe) with lower cost (Hesse, 1984).
Furthermore, it improves the physical properties of the soil and represents an environmental friendly agronomic practice. As a consequence, these economic, agronomic and environmental advantages have encouraged the launch of actions involved in the promotion and diffusion of good agricultural practices, such as the proper management of cattle droppings in order to prevent water sources from nitrates pollution. This paper aims at assessing the response of rice crop to the fertilization with pig slurry and at establishing criteria for the dosage.
2. Materials & Methods The experimental site was located in a 2 ha-plot, in Ebro Delta rice field area, located on South-east of Catalonia (Spain) (40º43’58” N, 0º 45’56” E, at 0 meters above sea level). The medium-term organic and chemical fertilization field experiment was established in 2007; where only mineral fertilisation had been previously used. The experimental design was laid out with a split-block with three replicates, in which the main plot and subplot were the basal and top dressing fertilization, respectively. The area main plot area was 780 m2 and the subplots were 390 m2. Rice variety was Gleva, widely grown in Ebro Delta rice fields, at a seeding rate of 182 kg seeds ha-1. Agronomic practices were identical to local management practices by commercial rice farmers.
The basal fertilization treatments were as follows: control (no basal fertilization), mineral fertilization (120 Kg N ha-1 as ammonium sulphate at 21% N), low rate of PS (90 Kg N ha-1), medium rate of PS (130 Kg N ha-1) and high rate of PS (170 Kg N ha-1). Top dressing fertilization (40 Kg N ha-1) with ammonium sulphate at panicle initiation was compared with the no fertilizer application. Paddy soil properties before experiment initiation were as follows: pH: 8.2, organic matter: 4.0 %, USDA classification: silty clay. The annual average temperature is 18ºC. The pig slurry was previously analyzed to determine the N, P2O5 and K2O content (Table 1).
The highest panicle density was obtained to the high dose of pig slurry with no difference respect to the mineral fertilization dose (Figure 1). The significant differences in yield were only observed between the control and the rest of basal fertilization treatments (Figure 2) which provided similar values. There was a positive response to increase the dose of N. The pig manure fertilization has not affected any studied treatments to the seedling establishment, development of pests and diseases or at weeds infestation (data no presented). The yield was increased 7% in plots with top dressing application (40 Kg ha-1) significantly.
4. Conclusion There is a good agronomic response of rice crop to pig slurry fertilization. The doses between 130kg N ha-1 are recommended. Pig slurry fertilization no influence on seedling establishment, disease sensitivity, nutritional status, weeds infestation and yield.
References Hesse, P.R. 1984. Potential of organic materials for soil improvement. In organic matter and rice. IRRI. Los Baños, Laguna, Philippines. 35-42.
Ministerio de Medio Ambiente y Medio Rural y Marino. http://www.marm.es Meelu, O.P. and Morris, R.A. 1987. Integrated management of green manure, farmyard manure, and inorganic nitrogen fertilizers in rice and rice-based cropping sequences. In Efficiency of nitrogen fertilizers for rice. IRRI. Los Baños, Laguna, Philippines. 185-193.
Nitrogen Workshop 2012
Using canopy reflectance to determine appropriate rate of topdress N in potatoes Van Evert, F.K.a, Van der Schans, D.A.b, Malda, J.T.c, Van den Berg, W.b, Van Geel, W.C.A.b, Jukema, J.N.b a Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands b Applied Plant Research, Wageningen University and Research Centre, Wageningen, The Netherlands c ALTIC, Dronten, The Netherlands
1. Background & Objectives Potato (Solanum tuberosum L.) growing contributes significantly to nitrate pollution of groundwater in The Netherlands. It has been shown that N savings of 25-30 kg N ha-1 without a negative effect on yield can be achieved by using a low basal N rate followed by a sidedress N application based on a measurement of canopy reflectance (Booij and Uenk, 2004; Van Evert et al., 2010). This method has been developed for cv. Bintje on a sandy soil, but in order to be of practical use, the method needs to work with a variety of soils and cultivars, and with the various sensors that farmers have at their disposal. The objective of the work reported here was to extend the reflectance-based N sidedress system to different cultivars, different potato use types (ware, starch, seed), different soils, and assess the usefulness of three different sensors.
2. Materials & Methods Experiments were established in several locations in The Netherlands in 2010 and 2011. They comprised sand and clay soils; ware, starch and seed potatoes; and several cultivars. In all experiments, reflectance measurements were made with the Cropscan (Cropscan Inc., Rochester MN, USA) and the Weighted Difference Vegetation Index (WDVI; Clevers, 1989) was calculated;
in one experiment, additional reflectance measurements were made with the N-Sensor (Yara International ASA, Oslo, Norway), Greenseeker (Trimble Inc. Denver, CO, USA) and CropCircle (Holland Scientific Inc., Lincoln NE, USA). Fresh tuber weight and dry matter content were determined at harvest in all experiments, while N uptake was determined through destructive sampling in some. Response curves were fitted where the data allowed and Nopt, the N rate at which maximum yield would have been obtained, was determined.
3. Results & Discussion The relationship between WDVI and N uptake is shown in Figure 1. Symbols represent measurements throughout the season in the various experiments. The broken line represents the relationship that was developed in earlier work for cv. Bintje on a sandy soil. Statistical analysis showed that inclusion of soil type, cultivar or potato use type, did not improve the regression.
The N rate in the reflectance-based N sidedress system was in most cases closer to Nopt than either the recommended N rate or the N rate in a petiole nitrate-based sidedress system. The sign and magnitude of the difference between Nopt and the reflectance-based N rate was variable. In particular, when Nopt was higher than the recommended N rate, this was not adequately detected by the reflectance-based system.
In one experiment, N uptake at the time of sidedress (first week of July) was low and largely unaffected by basal N rate. There was no response to sidedress N. It seems that N uptake had been hampered by dry conditions in June, leading to pooling of soil mineral N. Rain in July allowed rapid uptake of soil mineral N and made sidedress N superfluous. This highlights a serious limitation of reflectance-based systems: they are unable to measure the status of the soil. Including a
measurement of soil mineral N would overcome this limitation, but is unattractive from the point of view of cost and the labour this involves.
Figure 1. Relationship between WDVI and N uptake in potato.
See main text for further explanation.
The relationship between N uptake and sensor-specific vegetation index for several sensors is shown in Figure 2. The NDVI measured with Greenseeker turned out to be a poor predictor of N uptake; NDVI measured with Cropcircle is a better predictor. The vegetation index S1 measured with the N-Sensor is as good a predictor of N uptake as the WDVI measured with Cropscan.
Figure 2. Relationship between aboveground N uptake and vegetation index measured with (from left to right) Greenseeker, Cropcircle and N-Sensor.
4. Conclusion For a reflectance-based N sidedress system which allows N savings of 25-30 kg N ha-1 without a negative impact on yield, the relationship between vegetation index and N uptake is not affected by soil, potato use type, or cultivar. Not all sensors discriminate potato N status equally well.
Booij, R. and Uenk, D. 2004. Crop-reflection-based DSS for supplemental nitrogen dressings in potato production, in:
D. Mackerron,and A. J. Haverkort (Eds.), Decision support systems in potato production: bringing models to practice., Wageningen Academic Publishers. pp. 46-53.
Clevers, J.G.P.W. 1989. The application of a weighted infrared-red vegetation index for estimating leaf-area index by correcting for soil-moisture. Remote Sensing of Environment 29,25-37.
Van Evert, F.K., Jukema, J.N., Ten Berge, H.F.M. and Van den Berg, W. 2010. Using Reflection Measurements and Modeling to Minimize N Application in Potatoes [available online at http://a-c-s.confex.com/crops/2010am/ webprogram/Paper58591.html. Accessed 24 April 2012]. ASA/CSSA/SSSA Annual Meetings, Long Beach CA.
Nitrogen Workshop 2012
Using data management systems to facilitate better nutrient management planning on Irish farms Mechan, S.a, Lalor, S.T.J.b, Shine O.a, Jordan, P.c, Wall, D.b a Agricultural Catchments Programme, Teagasc, Johnstown Castle, Co. Wexford, Rep. of Ireland b Teagasc, Crops Environment & Land Use Programme, Johnstown Castle, Co Wexford, Rep. of Ireland c School of Environmental Sciences, University of Ulster, Coleraine, N. Ireland
1. Background & Objectives The Agricultural Catchments Programme (ACP) uses an innovative geo-computational information management system based around geographical information systems (GIS), for coordinating nutrient management planning on farms. Farm fertiliser planning for nitrogen (N) and phosphorus (P) is mandatory under European Union (EU) Nitrates Directive rules (Statutory Instrument (S.I.) 610 of 2010) in Ireland. This legislation constrains N and P applications of fertilisers on farms. Alongside these legislative constraints, the cost of fertiliser has increased continuously in Ireland since 2000 forcing farmers to re-evaluate their fertiliser input strategies in order to optimise fertiliser usage. To facilitate increased fertiliser use efficiency, the development and use of a farm nutrient management plan (NMP) is one strategy for maximising the return from on- and off-farm fertiliser resources and this has the potential to yield a double-dividend such as protecting the quality of nearby water resources.
To date however, developing an NMP for a farm has been a cumbersome task, usually requiring the collection of data from a number of disparate sources, and resulting in complicated and lengthy spreadsheet outputs. The ACP was initiated to evaluate the EU Nitrates Directive regulations in Ireland and has established experimental infrastructure in five small river catchments (c.500 to 1,200 ha) and one larger catchment over karstic bedrock contributing to a spring (2,990 ha) (Fealy et al., 2010; Wall et al., 2011). There are between 35 and 80 farms in each catchment and the ACP integrates research and advice in partnership with farmers and other stakeholders, facilitating productive agriculture within a framework for protecting water quality. This paper discusses the development of a novel prototype farm nutrient data management system which aims to facilitate better farmer buy-in and usability and improved nutrient management practice and to maximise nutrient use efficiency and recovery on farms.
2. Materials & Methods On the catchment farms, data collection included detailed nutrient application records, farm fertiliser plans, previous nutrient management plans, and physical farm attributes, e.g.
number and area of fields, livestock type and number, crop type and area, soil type and soil nutrient status, annual organic manure production. The fields and land management units within each farm were then digitised using ArcGIS 9 ArcInfo version 9.3. A soil census was conducted to develop a spatial representation of the soil P, K (potassium), Mg (magnesium) concentrations, soil pH and lime requirement (LR) for each catchment. Fields were coded spatially to identify soil test sample areas (~2 ha) and related geodatabases were developed.
The soil analysis results were retrieved using a laboratory information management system (LIMS) and linked with the geodatabase and nutrient management planning software to develop nutrient management plans. Nitrogen and P balances were calculated for each field/farm area to quantify farm nutrient loading and identify areas at higher risk for nutrient loss to water courses. Farm data collection and the development of new NMPs were facilitated by the ACP advisory team in each catchment. A Nutrient Management Recorder (NMR) was developed to capture nutrient application events on a per field basis. All data collected are stored centrally on a Document Management System (DMS) - MS SharePoint, which provides a secure system that collates the data into a centralised database. These
various farm data can be linked back to their spatial origin (e.g. soil sampling areas) using a structured geodatabase for each catchment.
3. Results and Discussion The ACP has created a more automated system that not only offers a farmer a NMP but also the facility to create maps representing the numerical data outputted from these plans. Maps can facilitate spatial representations for application rates for individual fields on a whole-farm basis in accordance with a soil census reports (Figure1 and 2). The ACP also developed a facility to capture day-to-day management events (e.g. fertiliser applications) on every farm within the catchments using a NMR. With such systems in place within the catchments, farmers are better equipped to plan their nutrient management strategies for the future, and also to track their progress and make informed changes to their plans when needed. Better farm nutrient management brings with it many production, economic and environmental benefits, including reductions in fertiliser wastage from over application, cost savings from reduced fertiliser inputs and potential reductions in nutrient losses to water bodies and to the atmosphere. The ACP Advisors have observed that the catchment farmers engage with the nutrient management information to a greater extent when presented spatially (on maps) for their farms and fields. They also have greater confidence that the nutrient management advice administered this way is carried out more accurately and in a more informed manner.
Figure 1. Colour coded spatial representation of soil Figure 2.
Crop and soil test specific P fertiliser and slurry test P indices for a farm. Also shown are the soil test application advice for a farm.
P concentrations (Morgan’s P) for each field.
4. Conclusions The development of a geo-computational information management system to collate and manipulate multiple farm nutrient source and geo spatial data sets has enabled farmers, advisors and researchers to fully utilise these data. This technology can be used to overlay many years of soil analysis results enabling researchers and advisors to track temporal changes in soil fertility and nutrient management. It facilitates integration for geospatial analysis and other research against a wide variety of other datasets whilst maximising the integrity of the data.
References Fealy R.M., Buckley C., Mechan S., Melland A., Mellander P.-E., Shortle G. and Jordan P. 2010. The Irish Agricultural Catchments Programme: catchment selection using spatial multi-criteria analysis. Soil Use and Management 26, 225-236.
Wall D., Jordan P., Melland A.R., Mellander P.-E., Buckley C., Reaney S.M. and Shortle., G. 2011. Using the nutrient transfer continuum concept to evaluate the European Union Nitrates Directive National Action Programme. Environmental Science and Policy 14, 664-674.
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