Swedish Farmers' Experiences of the Yara N-Sensor ABSTRACT

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1 Swedish Farmers' Experiences of the Yara N-Sensor M. Söderström, K. Nissen, K. Gustafsson, T. Börjesson and A. Jonsson Swedish Farmers Supply and Crop Marketing Association Lidköping, Sweden L. Wijkmark Rural Economy and Agricultural Society in Halland Lilla Böslid, Eldsberga, Sweden ABSTRACT The Yara N-Sensor estimates biomass and nitrogen demand from reflected light at different wave lengths. Scanning, estimation and fertilization is carried out on-the-go. The equipment has been increasingly used in Sweden since 1998, and a growing number of farmers have tested the sensor in practice. In this paper we discuss experiences from Sweden and also some new applications of the sensor. In 2003 about 600 farmers fertilized approximately 25,000 ha of mainly winter wheat, using the recommendations from the sensor. To investigate the users experiences of the sensor, opinion polls was distributed among farmers and contractors after the growing season. An economical model showed that scanning and fertilization of at least 250 ha is required for the investment in a sensor to be profitable. A number of new applications and uses of the N-Sensor have been developed or are under development, for instance: 1) prognoses for within-field variation of protein content in winter wheat and malting barley; 2) recommendations for variable-rate spraying of fungicides in cereals according to scanned biomass variability; and 3) site-specific N-fertilization as well as application of haulm killer in potatoes. Keywords: Yara N-Sensor, nitrogen requirement, farmers experiences INTRODUCTION The Yara N-Sensor (the name of the system was Hydro N-Sensor until April 2004 when Hydro Agri became Yara) is today extensively used in Sweden as a tool to provide information for spreading supplementary nitrogen-fertilizers according to within-field differences in demand. The Yara N-Sensor is designed for continuously measuring the crop s nitrogen demand, estimating the nitrogen requirement and controlling the amount of fertilizer from the spreader (Reusch, 1997).

2 The goal is to optimise the applied amount of fertiliser to the local requirement of the crop, so to benefit both economically and environmentally. It is also possible to store data from the performed work and to produce maps of the fertilization. If the N-Sensor is used without a spreader and spreader computer it can be used as an independent scanning system that registers the nitrogen demand as well as the biomass. Such maps can provide the basis of the farmer s own fertilization strategy. A number of tests have shown that the use of the N-Sensor can result in increased yield, more homogenous protein content and reduced nitrogen discharge (e.g. Dampney et al., 1999; Jürschik, 1999; Wollring et al., 1999; Wollring and Reusch, 1999; Ludowicy, 2002). However, in some other studies, it has not been possible to see such effects (e.g. Jørgensen and Jørgensen, 2001; Kilian et al., 2001). Under certain conditions the sensor recommendation may not prove to be optimal, for example when there is an unexpected water deficit in parts of a field, or sulphur or manganese deficiency. It is important to evaluate the economical potential of precision agriculture at the farm level. Farmers will in general not adopt this crop production technology if it is not profitable. In 2003 about 600 farmers fertilized approximately 25,000 ha of mainly winter wheat, using the recommendations from the N-sensor. The objective of this paper is to present some of the experiences and opinions from Swedish farmers that have used the N-Sensor during the last years. We will also present some new applications of the N-Sensor as well as a simple estimation model in order to facilitate the assessment of costs and incomes associated with such and instrument at the farm level. SYSTEM DESCRIPTION The unit consists of two diode-array spectrometers, fiber optics and a microprocessor in a rugged housing mounted on top of a vehicle s roof (Reusch et al., 2002) (the blue unit in Figure 1). One spectrometer registers light reflectance on four spots around the vehicle through fiber optics (two on each side, directed obliquely outwards (64 from nadir)). The second spectrometer senses irradiance from the sky hemisphere and registers ambient light in order to compensate for varying light conditions. Solar azimuth effects as well as shadowing effects from the vehicle are largely avoided by the oblique viewing angle (Reusch, 2003). One scan per second (the scanned area is about 50 m 2 ) is normally collected and registered in a user terminal mounted inside the vehicle. For optimal performance, the solar elevation should exceed 25. The user of the system will get an estimate of the N-requirement of the crop as well as a biomass-index from the terminal. These estimations are based on two preset quotients ( S1 and S2 ). It is not publicly known how S1 and S2 are determined, but they can be regarded as vegetation indices analogue to e.g. NDVI, RVI and others. S1 is mainly correlated to the chlorophyll content of the crop whereas S2 is correlated to the biomass (Hydro Agri, 2000). It is not possible to retrieve data from specific wave lengths with the standard sensor. A special variety of the sensor, Yara FieldScan, can be used for that purpose (Reusch et al., 2002).

3 Ambient light Reflected light sensors (two on each side) Ambient light sensor a b Fig. 1. Overview of the Yara N-Sensor system, a) from the side and b) from above (b from Ludowicy, 2002). The N-sensor terminal sends data on nitrogen requirement to the application rate controller that will adjust the spreader according to this information. A timedelay factor can be specified in order to consider the speed of the vehicle and the distance between the sensor and the spreader (figure 1b). Before scanning the sensor is calibrated. The calibration can be done in different manners, but most commonly the farmer together with the sensor entrepreneur selects an area for which they decide the N-requirement (Dref in figure 2), either from experience or with the help of an hand-held N-tester (Yara).

4 In the N-sensor terminal, the right part of the calibration function (the blue or dashed part of the curve in figure 2) is shifted vertically to fit the measured reference sensor value and Dref. A maximum and minimum N application rate is set. So S1 determines the N-demand except in areas with a poorly developed vegetation, below the biomass cutoff (figure 2), where the N-demand is determined by the S2 value, resulting in a decreasing N-application. This means that the N-requirement is determined relative to the calibration value. There are preset calibration functions for a number of crops, for instance cereals and canola. Maximum (DMax) N-requirement Reference (DRef) Minimum (DMin) S2Min Biomass cutoff (S2Max) Reference sensor value (S1Ref) S1 S2 Fig. 2. Before scanning the sensor is calibrated in the field whereas Dref is specified by the user. The figure shows the relationship between S1, S2 and N-demand. When the S1-value reaches a certain low level (the biomass cutoff), the N-requirement is determined by S2. THE USE OF THE N-SENSOR IN SWEDEN The number of sensors has increased since it was introduced in The first two years it was mostly used in various farm tests and development projects but in 2000 it became a commercial product. Between 2000 and 2003 it was available to the farmer through contractors who did the scanning and fertilization. Maps of the N-fertilisation and biomass are produced via a web-mapping service (Yara SensorOffice). The application of the N-Sensor in practical use is the single most successful precision agricultural technique in Sweden. It is far more accepted in general as a useful tool compared with for instance yield mapping. The scanned and fertilized acreage in 2003 was ha (figure 3). The instrument is mostly used in winter wheat (more than 80% of the acreage). Most of the fields that are scanned are not very large. The average field size is only about 20 ha. Figure 4 is a compilation of fertilization data from 135

5 randomly selected fields that was scanned and fertilized using the N-Sensor in The average standard deviation is about 11 kg N/ha, which means that the variation of N-requirement in most fields are within +/-22 kg N/ha about the mean. Fig. 3. Number of N-sensor units and scanned acreage in Sweden, Fig. 4. Average distribution of supplemental N-fertilization using the Yara N-Sensor in 135 fields in Sweden. The data in the table are mean values of the summary statistics for each field.

6 FARMERS OPINIONS A few opinion polls have been made among the farmers as wells as among the contractors during the years that the N-Sensor has been in use. The questions have not been exactly the same each time which makes it difficult to compile all results. Here we have chosen to report the answers on a few of the questions, mainly from the more extensive farmer surveys that are valid for 2000 and 2001 (ca 200 and 400 questionnaires were sent the first and the second year, respectively). The surveys were sent to all users and the response rate was about 50%. It can be seen that most farmers are relatively satisfied with the N-sensor service. Almost 70% believe that the N-Sensor fertilization most certainly resulted in an increased yield (bars 1 and 2 in figure 5). Less than 10% thought there were a yield decrease. For the question about a more uniform protein content the answers were fairly similar. An even higher percentage is of the opinion that they had reduced lodging in the N-Sensor fields. Growth regulators are not permitted in Sweden in wheat wherefore lodging can be a substantial problem some years if too much nitrogen is applied. Fig. 5. Answers to the question: How would you describe the result from the variable-rate application with the N-Sensor as compared with conventional application? Answers are given concerning increased yield, more uniform protein content and reduced lodging. When we instead look at the farmers conception on profitability the picture is somewhat less positive (figure 6). Slightly less than 25% seems to be doubtful concerning the profitability of the N-Sensor, but still an overwhelming majority thinks that it is economically sound to use the equipment. Only 11% of the respondents state that they will not use the N-Sensor service again (figure 7).

7 Fig. 6. Answers to the question: How would you describe the profitability of the N-Sensor service? Fig. 7. Answers to the question: Do you consider to use the N-sensor service again? The answers given by the farmers in figure 5 was more or less supported by reports from the N-Sensor contractors (table 1). The contractors were asked in 2003 about their experiences on how the use of the sensor influenced the crop and fertilizer consumption. 20 out of 28 responded and 16 of them stated that the fertilizer consumption was the same or reduced when they used the sensor. Two of them said the consumption increased. Most of them answered that the crop and the quality of the crop became more uniform. They were, however, in general not equally convinced that the yield increased.

8 Table 1. Answers from 20 of 28 N-Sensor contractors concerning their opinion of the effects of the N-Sensor fertilization in 2003 (when they treated about 25,000 ha) Increased/ No Decreased No opinion improved change Yield More uniform crop More uniform quality Fertilizer consumption NEW APPLICATIONS A number of new applications and uses of the N-Sensor have been developed or are under development, for instance: prognoses for within-field variation of protein content in winter wheat and malting barley, recommendations for variable-rate spraying of fungicides in cereals according to scanned biomass variability, and site-specific N-fertilization as well as application of haulm killer in potatoes. In some parts of Sweden required criteria for protein contents are sometimes not reached. In malting barley % is optimal, while milling wheat should normally have a protein content of at least 11 %. Since 2003 the sensor has been used for protein prognoses. The best results have been obtained when scanning is done at Zadok development stage 69 (Börjesson and Söderström, 2003). From 2004, a calibration function for protein prognoses has been included in the N- Sensor. However, still it seems to be difficult to correctly estimate the exact protein level, but it is often possible to find areas of relatively low or high protein content (Börjesson and Söderström, 2003). Generally the optimum application rate of fungicides is related to the density of the crop. This means that the biomass map of the N-Sensor may be a suitable tool for estimating the variable-rate requirement of fungicides. The biomass data estimated by the N-Sensor is exported from the terminal and recalculated to a fungicide demand map. It is an ongoing activity to determine the most suitable recalculation function. The recalculated map can then be used in the N-sensor terminal to control the spraying. In a similar manner the scanning of biomass can be the basis of variable application of haulm killer in potatoes. The N-Sensor has also a potential to be used for N-fertilzation in potatoes and sugar beets (Lokhorst et al., 2001).

9 INVESTING IN A N-SENSOR We have seen that the general perception of the Swedish farmers who have used the N-Sensor is that N-Sensor-based supplemental variable-rate N- fertilization generally results in yield increase, reduced risk for lodging and a more uniform protein content and according to the contractors, this is realized without increasing the average N-application. It also yields valuable documentation of fertilization and within field variability of biomass. Moreover, according to some farm tests, it has been shown that the threshing capacity generally is increased, on average 15-20% (Feiffer Consult, Nordhausen/Sondershausen, Germany, unpublished). Since last year, Swedish farmers can buy their own N-Sensor. In table 2 is presented a compilation of costs and general value of some possible benefits (developed from Nissen et al., 2002). The calculation is made for three different farm sizes. Table 2. Economical assessment of costs vs. benefits for a farmer to invest in a N-Sensor (in Swedish Kronor, SEK. Presently 1 US$ = 7.50 SEK; 1 Euro = 9.15 SEK). Yearly use (ha) Depreciation (years) Benefits Yield increase (SEK/ha) Reduced lodging Uniform quality VRA of fungicides VRA of growth regulators VRA of haulm killers Increased threshing capacity (SEK/ha) Information value (SEK/ha) Others Sum (SEK/ha) Costs Interest rate (6%) 5, Depreciation, 165,000 in 5 yrs N-sensor support and licenses Service and maintenance 33, , , Sum (SEK/ha) Total SEK/ha Total SEK/farm 15,000 80, ,000

10 The estimated average yield increase is 2.4% which is the average result from 122 trials during (Hydro Agri, 2002). Note that we have not put in any incomes on a number of rows in the table, since it depends on the use of the sensor and the conditions of the farm. Still, according to this calculation it seems as if an acreage of 250 ha is sufficient to cover the costs of the investment. CONCLUSIONS The N-Sensor has rapidly become one of the most used precision agricultural techniques in Sweden. The relative success is the simplicity and that the farmer easily can see the potential benefits. Most of farmers that responded to questionaires believed that the yield increased, the quality became more uniform and lodging were reduced. A few new applications of the sensor is under development. Protein prognoses in malting barley seems to be especially interesting. It should be interesting for many farmers to buy their own equipment. An economical assessment showed that 250 ha normally is sufficient to pay the equipment. REFERENCES Börjesson, T. & Söderström, M Prediction of protein content in cereals using canopy reflectance. In: Stafford, J. & Werner, A (eds.): Precision Agriculture. Proceedings of the 4th European Conference on Precision Agriculture, Berlin, Germany. Wageningen Academic Publishers. p Dampney, P., Froment, M., Moore, M., Stafford, J. & Miller, M Ertragskartierung und teilflächenspezifische Pflanzenproduktion. ADAS Boxworth, Cambridge, UK. Hydro Agri Precise: a sixth sense for agriculture. Hydro Agri, Upton, UK. Hydro Agri Hydro N-Sensor, Växtodlingsåret Information om gödsling med Hydro N-Sensor. Unpublished information material (in Swedish) Jørgensen, J.R. & Jørgensen, R.N Impact on grain quality parameters when nitrogen is sensor applied by the Hydro Precise system. In: Grenier, G. & Blackmore, S. (eds.). ECPA Third European Conference on Precision Agriculture (vol. 2). Montpellier, France Jürschik, P Teilflächenspezifische Düngung - Grundlagen, Konzepte, technische Lösungen. DLG Merkblatt 315, Frankfurt am Main, Germany. Kilian, B., Hurley, T.M. & Malzer, G Economic aspects of precision agriculture: An ecenomic assessment of different site-specific N- fertilization approaches. In: Grenier, G. & Blackmore, S. (eds.). ECPA Third European Conference on Precision Agriculture (vol. 2). Montpellier, France Lokhorst, C., Kasper, G.J. & Sonneveld, C Possibilities for the Hydro-Agri N-Sensor in Dutch potatoe, sugar beet and grass production systems. In: Grenier, G. & Blackmore, S. (eds.). ECPA Third European

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