# 4.  Methods and Materials

## 4.1. Measurement Principle

The measurement principle, first described by EHLERT & SCHMIDT (1996), is the basis for the advanced measurement principle described in this work.

## 4.2. Mechanical Equations

The following mechanical formulas (BEITZ & KÜTTNER 1990) are used for discussing the measurements based on the simplified theory seeing the cereal stem as a cantilever:

for the mass moment of inertia of the stem at the fixed point B, the root, according to STEINER is equation 1

 J B = J G + m e 2 [1]

where J B is the mass moment at the fixed side at point B, then J G is the mass moment in the gravity point G of the stem, m is the mass of the stem, and e is the height of the gravity point G of the stem to the soil. From equation 1 is calculated equation 2 for the mass moment of the stem at the height of the contact with the pendulum

 [2]

where F is the force due to the mass moment of inertia, v D is the driving velocity, r is the height of the contact point of the cylindrical body with the stem, and x is the bending distance of the bended stem, and J B is the mass moment at the fixed side at point B. In long bending distances there will be an additionally force due to the acceleration of the mass of the upper parts of the stem, expressed by equation 3

 F = m a [3]

where F is the force necessary to accelerate the stems, m is the mass of the accelerated parts of the stem, and a is the acceleration of the stems during measurement.

For the bending moment of resistance of the stem is used equation 4 to calculate the force F necessary to bend the stem

 [4]

where E is the elasticity of the material, r is the height of the contact point of the cylindrical body with the stem, x is the bending distance of the bended stem, and I is the second moment of the [page 10↓]area. The elasticity is a measure of material property, defined as the proportionality constant relating stress to strain within the elastic range of a linearly elastic material.

The second moment of the area I is a measure of the distribution of mass around the longitudinal axis of a structure, hence dependent upon transverse geometry, and is calculated for hollow cylinders using equation 5

 [5]

where do is the outer diameter and di is the inner diameter of the stem.

The bending distance x is the distance at which the stem is in contact with the pendulum and it can be derived from equation 4

 [6]

where F P is the force of the pendulum, r is the height of the contact point of the cylindrical body, E is the elasticity of the material, and I is the second moment of the area.

Friction force is the product of the mass of a sliding body and the friction coefficient of its sliding surface. The friction force can be derived by equation 7

 [7]

where F F is the friction force, where F V is the vertical force component of the pendulum, and µ G is the friction coefficient between the two surfaces of the stem and the pendulum.

## 4.3. Apparatus and Carrier

A light weight four wheel construction, consisting of two bicycles, welded together with a square frame, served as a carrier. Figure 1 provides a good illustration of the research carrier. The height of the frame being adjustable to keep it above the crop. The carrier has a handle to push the carrier manually along the rails. The bicycle wheels ran on rails to ensure exact sideways position in the replicated experiments.

The carrier was equipped with the measurement sensors, an electronic cabinet, two 12 Volt batteries as energy supply, and a 486 laptop to record and save the data. All original data was saved, and the vast measurement data was not reduced by the measurement system, but later by an Excel Visual Basic macro

## 4.4. Software

The measurement software was the program Nextview, using the measurement system µ‑Meter 4, produced by BMC-Systeme GmbH. The measurement system had four inputs for analogue signals, and was calibrated for each sensor individually. It recorded the measurements of all four sensors as numerical data, together with the measurement time, and saved them as ASCII files.

## 4.5. Statistical Methods

An Excel Visual Basic macro reduced the original measurements into averages per plot, marked in the file by the trigger, thus reducing the data material to a single value per plot.

Based on this plot average all further statistics were calculated:

• The sample size of the replicates necessary for the parameter optimisation trials, was calculated according to KÖHLER et al. (1992) for comparing two averages of two different samples with the same standard deviation, based on equation 8

 [8]

where n is the unknown sample size, s is the standard deviation in preliminary tests, and is the smallest still interesting detectable difference of the measurement value angle of [page 12↓]deviation, and t (2n-2; α ) and t* (2n-2;ß) are the values of the two sided t-table for the errors of first and second kind, here used for a 5% error.

• As reduction method to reduce the vast number of measurements to values per plot, were the average angle of deviation, the median of the angle of deviation, and the average of the vector calculated in Excel 7.0. The vector of the angle of deviation was calculated based on following equation 9

 [9]

where x i is the length of the horizontal vector component, y i is the length of the vertical vector component, and n is the number of measurements to be reduced.
• Observed and predicted residuals were plotted in SPSS 7.0 to check for possible biases in the measurement values.
• Standard deviation and coefficient of variation of the measurement replicates of the plot average angle of deviation were calculated in Excel 7.0 using a Visual Basic Macro.
• The relationship between biomass and angle was determined using simple linear regression (Pearson regression), to calculate linear goodness of fit (equation 10) and square goodness of fit (equation 11), and calculate the standard error of estimate of the regression formula (square root of the residual mean square), and its significance in STATISTICA 5.0.

 y = a + bx [10] y = a + bx + cx 2 [11]

where y is the angle of deviation, x is the biomass, a is the intercept of the regression equation, b is the linear regression coefficient, and c is the square regression coefficient.
• The relationship between additional plant parameters and angle was determined using simple linear regression (Pearson regression), calculated in STATISTICA 5.0.
• The relationship between pendulum settings and angle was determined using simple linear regression, and multiple stepwise forward regression calculated in STATISTICA 5.0.
• The interpolation of the crop biomass map in the Baasdorf winter wheat field was done in ARCVIEW 3.1, with the interpolation method inverse squared distance weighted IDW,

## 4.6. Determination of Growth-Stages

The growth-stages were determined according to the BBCH code (WITZENBERGER et al. 1989, LANCASHIRE et al. 1991). The BBCH code was developed from ZADOKS code [page 13↓](ZADOKS et al. 1974) and is more accurate than the former. With regard to rice, the usual days after transplanting DAT, or days after seeding DAS, is used equivalently to the BBCH code. The BBCH code is used throughout this text, and a figure with the BBCH code, the days after transplanting DAT, and the name of the growth-stage is given in the appendix.

## 4.7. Determination of Biomass as Fresh Mass and Dry Mass

Fresh mass was determined by cutting all plants in a specific plot at soil surface, and weighing immediately thereafter on a balance with an accuracy of at least 0.1 gram. The cutting of the plants was done mostly in the afternoon, when the plants were hand dry. Winter rye 1998 was cut with a motor mower, while all the other crops were cut with sickles. With regard to rice, the field was drained before cutting. The fresh mass of the plot was calculated in kg m-2.

Dry mass was determined for winter rye in 1998 according to method 1 used by KUHLA & WEIßBACH 1994, 48 hours oven drying at 60°C and then oven drying for 3 hours at 105°C. But since this is only possible for small samples, it was not useful to clarify the question, whether the biomass sensor was determining dry mass better than fresh mass, or vice versa. In this way, it was only possible to calculate the total dry mass per test area, based on the fresh mass and then multiplied with the determined dry matter content, thus biasing total dry mass.

With reference to irrigated rice, winter wheat 1999, and winter rye 1999, dry mass was determined according to the IRRI quality assurance (1996) for drying large biomass samples in cloth bags. Drying time for rice was 72 hours at 80°C, and 105°C during 72 hours for wheat and rye respectively. Repeatedly weighed test samples in cloth bags showed a constant weight after 65 hours. By using cloth bags, it was possible to determine the total dry mass of all plots. After weighing the fresh mass, the material was oven dried. The dry mass of the plot was calculated in kg m-2.

## 4.8. Determination of Additional Plant Parameters

### 4.8.1. Determination of Plant Height

Plant height was measured in centimetres from the top of the untouched plant – including awns – to the soil using a gauge-stick. The average plant height was considered to be the height of the main crop and was averaged out of 50 randomly selected stems of a plot.

### 4.8.2. Determination of Crop Density

Crop density in this context means, depending on the growth-stage, either the number of shoots (tiller density), or stems, ears, or panicles (stem density) per unit area. The account of crop density used in this work, was the number of stems counted on a full square meter per plot. [page 14↓]During rapid crop growth, it was for several tests not possible to count the number of the stems due to a lack of time. In the rice measurements, only the number of those tillers were counted that were touched by the pendulum.

### 4.8.3. Determination of the Number of Hills

In paddy rice is the number of hills a measure of the yield structure and is related to crop biomass, and therefore, the total number of hills was counted per square meter.

### 4.8.4. Determination of Fungi Infestation

Fungi infestation was determined for downy mildew (Erysiphe graminis) according to EPPO Standard PP 1 / 26 (3). Eye spot (Pseudocercosporella herpotrichoides) was determined according to EPPO Standard PP 1 / 28 (2).

## 4.9. Layout of Trials

### 4.9.1. Crops, Test Sites, Cultivation Methods and Climate

The biomass sensor has been tested with winter wheat (Triticum aestivum ssp. Vulgare), winter rye (Secale cereale L.), and irrigated rice (Oriza sativa L.). Test sites for winter rye and winter wheat were located in Germany, at or near the Institute for Agricultural Engineering ATB near Berlin (figure 2), while the test sites for irrigated rice were located at the International Rice Research Institute IRRI, Los Banos, near Manila, Philippines.

German test sites were situated in the temperate zone, characterised by long and strong winters, warm and dry summers with early summer droughts. The IRRI test sites for irrigated rice were situated in the sub-humid tropics with pronounced wet and dry season. The tested rice was irrigated tropical lowland rice in a rice-rice double cropping system. Winter rye and winter wheat were tested at the growth-stages BBCH 32-34, 39, 59, and 69. Rice crops were tested at the growth-stages BBCH 25, 39, 49, and 65, or 28, 42, 62, and 80 DAT respectively. Rice was also tested at BBCH 49, due to the time difference between BBCH 39 and 65.

Cultivation, management methods, and soil types of the German test sites for the winter rye and winter wheat optimisation trials are given in table 2. Soil types can have a wide range, depending on the field heterogeneity, and were not specified for the tests, since the sensor is not able to recognise the soils. The cultivation of the other test sites of winter wheat and winter rye are given in the appendix. The winter wheat optimisation trial was at the same time the test site of the trial for spatially reduced fungicide application in winter wheat.

 WINTER RYE 1997 / 1998 Königsfeld Variety: AMILO Soil types: sandy loam, loamy sand Preceding crop: Winter rye Date Name Application rate Seeding 30th October 1997 AMILO 120 kg ha-1 Fertilizing 25thMarch 1998 21 / 21 / 21 N P K 90 kg ha-1 nitrogen Growth-regulator 8thMay 1998 MODDUS 0.6 l ha-1 WINTER WHEAT 1998 / 1999 Grube field Variety: BATIS Soil types: loamy sand, clayey loam Preceding crop: Summer barley Date Name Application rate Seeding 12th October 1998 BATIS 80 kg ha-1 Fertilizing 31stMarch 1999 Calcium-ammonium-nitrate 137 kg ha-1 nitrogen Herbicide 2ndMay 1999 STARANE 0.7 l ha-1 Fungicide* 20thMay 1999 JUWEL TOP 1.0 l ha-1 Growth-regulator 20thMay 1999 MODDUS 0.4 l ha-1

* site-specific application in some parts of the field as site-specific field trial

Cultivation, management methods, and soil types of the IRRI test sites in the optimisation trials for irrigated rice are given in table 3. The cultivation of the other test site in irrigated rice is given in the appendix.

 IRRIGATED RICE Variety: IR 64 Research Farm: IRRI, Philippines, 1998 / 1999, Dry season Preceding crop: Irrigated rice, Double cropping system Date Name Application rate Transplanting 27th December 1998 IR 64, 14 days old DAPOG seedlings 33 hills m-2, 2-3 plants per hill Fertilizing 26th December 1998 Urea 65 kg ha-1 nitrogen Rat protection 15thJanuary 1999 Plastic fence Weeding 28thFebruary 1999 By hand

At the German sites near Berlin there is predominantly a sub-continental temperate climate, characterised by long cold winters and warm summers, often accompanied by droughts in spring or early summer. The water deficit is the most important factor hindering plant production. Daily precipitation and temperatures for the German test sites are given in figures 3 and 4 for the growing season of the years 1998 and 1999 respectively.

Both seasons were warmer than usually and exceptionally dry, with 22 mm of rain between the 8th April and the 24th May 1998, and 21 mm of precipitation between the 25th April and the 31st of May 1999, causing in both years considerable drought symptomes in the crops, and in the 1999 season even crop extinction on sandy soils.

At the Philippine sites at IRRI there is predominantly an oceanic sub-humid tropical climate, expressed with a dry season and a wet monsoon season. In the dry season there are occasionally showers, but mostly dry and sunny conditions. The water deficit is no factor in plant production due to irrigation. The monsoon season is characterised by high precipitation, as heavy showers or long-lasting rainfalls. Daily precipitation and temperatures for the IRRI test sites are given in figures 5 for the growing season 1998 / 1999.

The dry season 1998 / 1999 was influenced by the “La Ninja” weather phenomenon, causing a higher than usual rainfall in the wet season 1998, and prolonging the wet season into the dry season, literally changing the dry season month December into a wet season. The measured precipitation for December 1998 was 603 mm, the 20-year average was only 130 mm for this month. With the exception of October was the monthly precipitation considerably higher than the 20-year average for these months. In addition was the monthly average temperature 1-2°C higher than the 20-year average for the most months.

### 4.9.2. Optimisation Trials of the Biomass Sensor

The biomass sensor optimisation trials were set up in fields, where there was the highest visible difference in biomass (figure 6). This difference in plant mass was visible in winter wheat and winter rye through large differences in crop height, while in paddy rice it was visible in crop density, that means how far one can see through the crop. The optimisation trials in winter rye – variety AMILO – were done in 1998 at the ATB research field near Berlin, Germany, in winter wheat in 1999 – variety BATIS – at a farmers field near Berlin, Germany, and in irrigated rice – variety IR 64 – in the dry season 1998 / 1999 at the IRRI research farm in the Philippines.

At selected places with differences in biomass, the rails were laid out in order to run the carrier. Every 5 meters, plot marker sticks were set in the soil to push over the trigger, and thereby mark [page 19↓]the plots in the measurement data and in the field, thus ensuring that the biomass sensor had the same position in the crop in all replicates. Between the rails, the crop existed in an untouched natural condition with large differences in biomass. Thus, the plots were only visible through the plot markers along the rails. All stems and tillers, that were sideways out of reach of the biomass sensor, as well as damaged plants and weeds, were cut out of the crop before the optimisation trial started, thus ensuring that only the actual biomass influencing the readings was correlated with the measurement. Each plot was 5 m long and 1 m wide, therefore, altogether 5 m2 per plot. The optimisation trial strip was 60 meters long, resulting in 12 plots. The carrier was pushed manually along the rails with a speed of 2.5 m s-1, which was the lower speed of self-propelled field sprayers during application. The measurement frequency was usually 75 Hz, and the measurements were usually 5 times replicated.

Measurement Replicates in the Optimisation Trials

The measurements of the 12 plots in these optimisation trials were the basis for determining the accuracy of the replicates. The accuracy of the 5 replicates was determined by comparing the plot measurements obtained with the same pendulum parameter setting, and calculating the standard deviation and the coefficient of variation.

Dependency of the Measurement on the Biomass in the Optimisation Trials

The measurements of the 12 plots and the plant mass of the 12 plots of these optimisation trials were the basis for determining the accuracy of the biomass measurements. The relationship between the biomass measurements in 5 replicates and the destructively sampled and weighed biomass was determined by correlating the values of the 12 plot averages with the weights of the biomass in the 12 plots. The accuracy of the regression was determined by the goodness of fit and the standard error of the regression.

Dependency of the Biomass Measurement on the Sensor Parameter Setting

The dependency of the measurements on the pendulum’s parameters was determined by simple and multiple regression for the change of the plot measurement when the pendulum parameters were changed. Therefore, the regressions were calculated for one plot of the optimisation trials of the 5 replicates of each parameter setting.

Trials for the Relation of the Measurements to Additional Plant Parameters

In addition to the optimisation trials for biomass sensing, on the same test strip, further measurements were undertaken. For determining the relationship between other plant parameters, such as plant height or crop density, and the sensor measurements, the angles of [page 20↓]deviation, were correlated either with the number of stems per square meter or with the plant height. These relationships were not researched for all growth-stages since the focus of the work was the biomass sensing. The measured plot averages of the 5 m2 plots were correlated with the average number of stems in these plots or the average height of the plants. As with the optimisation trials the strip was 60 meters long, hence 12 plots, with 5 replicates. The carrier speed was 2.5 m s-1, and the measurement frequency was usually 75 Hz.

Trials for the Influence of the Carrier Speed on the Measured Angle

As with the determination of other plant parameters, the basis of the measurements were the test strips of the optimisation trials after the optimisation trial was finished. The tests for determining the influence of the carrier speed on the biomass measurements were done with one parameter setting and a measurement frequency of 75 Hz. In the tests only the carrier speed varied in increments of 0.5 m s-1. Tests started with 0.5 m s-1 as the lowest speed, passing on to 1.0 m s-1, 1.5 m s-1, 2.0 m s-1, 2.5 m s-1, 3.0 m s-1, and finishing with 3.5 m s-1 as the highest possible speed, each with 5 replicates. The measured plot averages of the 5 m2 plot were correlated with the carrier speed, and were measured repeatedly for all crops and different growth-stages. Only in winter wheat, at BBCH 49, was the influence of the carrier speed on the measurements taken, in all 12 plots of the test strip, and calculated for the individual plots, with the exception of the first three, where a speed of 3.5 m s-1 was not possible to test due to the short acceleration way.

### 4.9.3. Trials for Potentially Biasing Factors on the Biomass Measurement

Trial for the Effect of Wind on the Measurement

To determine the influence of wind on the biomass measurements, trials were undertaken with a singular parameter setting, a measurement frequency of 75 Hz, a carrier speed of 1 m s-1, for one 5 m2 plot, in rice at the growth-stage BBCH 69, or 80 DAT respectively. The influence of wind was not tested in other crops. In addition to the usual equipment on the carrier, two fans were attached to the carrier (figure 7). The wind of the fans was directed right onto the pendulum’s cylindrical body, the penetration angle of the wind into the crop canopy was 30° in this combination. The wind direction was opposite to the measurement direction. The fans were infinitely variable in speed, thus giving the possibility of measuring the crop with wind speeds of 0 m s-1, 1 m s-1, 2 m s-1, 3 m s-1, and 4 m s-1. The lower parts of the fans were covered with cardboard to prevent a threshing of the panicles. The wind speed was measured with an anemometer directly in front of the pendulum’s cylindrical body, at the centre and at both ends of the cylindrical body. The measured plot averages of the 5 m2 plot was then correlated with the wind speed.

Trial for the Effect of Water Height on the Measurement in Irrigated Rice

The influence of the water height of the irrigation water in irrigated rice was tested in rice at BBCH 39, or 42 DAT respectively, for one 5 m2 plot, using a measurement frequency of 75 Hz, and a carrier speed of 1 m s-1, and one single parameter setting. Water depth was determined using a measuring-stick, that gave the water height measured from the root base in centimetres, while the paddy field was flooded. The measurements were done during flooding. The measured plot averages of the 5 m2 plot was then correlated with the height of the irrigation water.

Trials for the Effect of Weeds on the Measurement

To determine the influence of weeds on the biomass measurements, trials were undertaken with a singular parameter setting in one 5 m2 plot, a measurement frequency of 75 Hz, and a carrier speed of 2.5 m s-1. The influence of weeds was tested in winter wheat. The effect of Creeping Thistles (Cirsium arvense (L.) Scop.) was tested in winter wheat BBCH 39 in a plot with one single thistle 0.1 m higher than the crop. The effect of Loose Silky-Bent (Apera spica-venti (L.) Pal. Beauv.) was tested in winter wheat at BBCH 69 in a plot with a large number of them. The measurements were done in 5 replicates with the weeds in the crop and later without them.

Trials for Biasing Factors of Influence on the Measurements

The objective of the trials for the influence of biasing factors on the measurements of the biomass sensor pendulum-meter was to observe whether there is a factor that is able to bias the [page 22↓]measurements or not. Since these were singular tests the results can only indicate which factor is biasing the measurements, but an exact determination of the magnitude of the bias due to these factors will have to be done in further research.

In the trials for the influence of the day and daytime the test strip analogue to the optimisation test strips was measured repeatedly during two consecutive days with a singular parameter setting, 2.5 m s-1 carrier speed, and 75 Hz measurement frequency. The measurements were done in winter rye at BBCH 39. The averages of the plots were arranged in their respective time of measurement for the two days to see the effect. To indicate the effect of the growth-stage and rapid growth rate of the crop, an identical parameter setting was used in two growth-stages in winter rye. The carrier speed was 2.5 m s-1, and the measurement frequency was 75 Hz for all measurements.

The trials for the effect of cultivar, variety, season, and year were made in all crops based on the results of the parameter optimisation trials. In winter wheat and rice, the effect of the variety was tested by measuring a different variety with an identical parameter setting as one of the settings used in the optimisation trials, a carrier speed of 2.5 m s-1, and a measurement frequency of 75 Hz. Thus, it was possible in rice to compare two different varieties, one seeded and the other transplanted, and one crop tested in the dry season while the other was tested in the monsoon season, both tested at BBCH 65, or 80 DAT respectively. With regard to winter wheat it was possible in this way to compare different varieties, one ZENTOS and the other BATIS, in two different years at BBCH 69. In winter rye, the effect of the year was also compared. In the first year the measurements were obtained in the optimisation trials, and in the second year measurements were replicated with one specific parameter setting to check for the repeatability of the measurements in succeeding years. The measurements in winter rye in the second year were not taken over a 60 m long test strip, or 12 plots, but over a 50 m long strip, or 10 plots.

The trial for the effect of the ratio of biomass versus crop height was done in winter wheat at BBCH 69, to check for the influence of the crop height versus the crop density. Therefore, a test strip with differences in crop height was measured with a specific parameter setting, 75 Hz measurement frequency and 2.5 m s-1 carrier speed. Then one plot was measured repeatedly, while the crop of the plot was randomly thinned out in increments of 25 stems between the measurements. Each measurement was replicated 5 times and averaged for the plot. The thinning results were then compared with the measurement of the entire test strip.

The trial for the influence of the stem inclination was done in winter wheat at BBCH 69 with identical parameter settings, 75 Hz measurement frequency and 2.5 m s-1 carrier speed. The [page 23↓]measurements were first done in one direction and then in the return direction, with 5 replicates. Thus, one measurement was obtained against the 30° inclination of the stems and the other vice versa same direction as the inclination of the stems. The averages of the measurements and their difference were then compared. Similarly was one plot in each parameter optimisation trial in rye and wheat tested in both driving directions, and the plot averages were then compared.

Tests for the Limits of Non-Destructiveness of the Biomass Sensor

Before the parameter optimisation trials were started, the parameters were tested for their limits at which they induce symptoms of destruction on the plants. Therefore, the sensor was moved with possibly critical parameter settings in the crop. Those parameters showing symptoms of destruction were usually not used in the parameter optimisation trials, but some parameters showed symptoms of destruction not in the first replicates, but in the 5th replicate.

### 4.9.4. Trials for Spatially Reduced Plant Protection

To investigate the potential of site-specific reduced plant protection, according to the biomass data measured by the biomass sensor pendulum-meter, two field trials were set up. One in winter rye, with a site-specific reduced application of the growth-regulator MODDUS®, and one in winter wheat, with a site-specific reduced application of the strobilurin-fungicide JUWEL®.

Site-Specific Application of Growth-Regulator

The trial consisted of two neighbouring tram lines, of which one was the strip with the site-specific reduced application, and the other one the strip with the uniform application of the growth-regulator MODDUS® (trinexapac-ethyl). In the uniform variant, the entire strip was sprayed with 0.6 l ha-1 MODDUS in 300 l ha-1 water. In the strip with the site-specific reduced amount of the growth-regulator MODDUS, the field sprayer was switched on and off. That means, in areas with low biomass, there was no application, and in areas with average or high biomass, there was a full application. In addition, there was a third tramline left without spraying of the growth-regulator as a control strip. Cultivation methods are given in the appendix. Plot length was 10 m, and both strips were 440 m long. Permanent plot markers in the crop indicated the plots till harvest. The carrier was pushed along the tramlines with a velocity of 1.0 m s-1. The measurements were done with the pendulum settings of 0.5 m hP, 0.1 m hA0, 1 kg m P, and a frequency of 75 Hz. After measuring the winter rye crop, variety AMILO, at BBCH 49, the average angles were calculated for one meter. Flags were set up in the crop, where the application rate changed according to the measurements, since an on-line control of the sprayer was technically not yet possible. Grain yield was determined by harvesting each strip separately in a harvester without a yield measuring device, and weighing the yield on a truck balance.

Site-Specific Application of Fungicide

The trial consisted of two strips along neighbouring tramlines, one for the site-specific reduced application, and the other one for the uniform application. The fungicide JUWEL® was applied, consisting of the two active agents kresoxim-methyl and epoxiconazol. For the other cultivation methods see winter wheat in table 1. In the uniform variant, the entire strip was sprayed with 1.0 l ha-1 JUWEL in 300 l ha-1 water. In the strip with the site-specific reduced application, the field sprayer alternated between full application, an application of 0.66 l ha-1 JUWEL, and the lowest rate using 0.33 l ha-1 JUWEL, according to the biomass data. Plot sample length was 10 m, and both strips are 320 m long. Permanent plot markers in the crop indicated the plot positions. The carrier was pushed along the tramlines in the field with a velocity of 1.0 m s-1. The measurements were done with the pendulum settings of 0.6 m hP, 0.3 m hA0, 1 kg m P, and a measurement frequency of 75 Hz. After measuring the winter wheat crop, variety BATIS, at BBCH 41, the average angles were calculated for one meter. The angle, at which the application rate changed, was determined according to a visual assessment of the specific risk of low and high biomass yields for fungi infestation. Flags were set up in the crop, where the application rates changed. Flags indicated the area with the same application rate, since an on-line control of the sprayer was not yet possible. The field sprayer varied application according to the flags. Grain yield was determined by harvesting each strip separately with a harvester without a yield measuring device, and weighing the yield on a truck balance. Fungi infestation of downy mildew was determined in the uniformily sprayed tramline.

### 4.9.5. Spatial Crop Biomass Map

In the year 2000, a prototype pendulum-meter was tested for a tractor-based, automatic on-line sensing of cereal crop biomass, which was attached to the three point linkage in the back of the tractor. The tractor ran in the tramlines while sensing the biomass. The measured crop was a winter wheat crop of the field Baasdorf at BBCH 39, variety CONTRA. Positioning was determined with a TRIMBLE® 132 GPS (INSAT 2000), and recorded with an advanced version of the NEXTVIEW® measurement program. The measurements were done with the pendulum settings of 0.6 m hP, 0.1 m hA0, 1 kg m P, a carrier speed of 2 m s-1, and the measurement frequency was technically limited to 1 Hz. Positioning data were recorded in World Geodetic System of 1984 – WGS 84 (EUROPEAN ORGANISATION FOR THE CONTROL OF AIR NAVIGATION 2000), and re-calculated in Gauß-Krüger as projector and Bessel as ellipsoid. A video of this measurement with the prototype pendulum-meter is attached.

 © Die inhaltliche Zusammenstellung und Aufmachung dieser Publikation sowie die elektronische Verarbeitung sind urheberrechtlich geschützt. Jede Verwertung, die nicht ausdrücklich vom Urheberrechtsgesetz zugelassen ist, bedarf der vorherigen Zustimmung. Das gilt insbesondere für die Vervielfältigung, die Bearbeitung und Einspeicherung und Verarbeitung in elektronische Systeme. DiML DTD Version 3.0 Zertifizierter Dokumentenserver der Humboldt-Universität zu Berlin HTML generated: 13.09.2004