Präsident der Landwirtschaftlich-Gärtnerischen Fakultät:
Prof. Dr. J. Mlynek
Dedicated to my wife Renate
Die Zukunft hat viele Namen.
Für die Schwachen ist sie das Unerreichbare.
Für die Furchtsamen ist sie das Unbekannte.
Für die Tapferen ist sie die Chance.
The future has many names.
For the weak it is the unattainable.
For the fearful it is the unknown.
For the brave it is the chance.
Victor Hugo (1802-1885)
Heterogeneity is a common phenomenon in nature. The vast majority of agricultural fields are heterogen and have large variations in parameters such as soil type, nutrient availability, slope, crop height, tiller density, plant mass and others within a single field. Spatial and temporal processes are causing this variability resulting in spatial variability (variability in space) and temporal variability (variability in time). Variability may be random or correlated, may be over a long distance or short distance, and may have a small or large magnitude. Arable crops respond to this variability to different extends that produce variations in quantity and quality of the cultivated crop and is finally visible by different yields. In general, the current practice of farmers is to treat fields uniformily regarding the applied inputs such as fertilizers and agrochemicals. It is obvious that this uniform application of treatments to an arable crop is not only inefficient in terms of costs, but also has a negative environmental result.
The pressures of environmental constraints and efficient use of treatments are driving farmers towards precision in arable crop operations. To reduce environmental damage and optimise the use of agrochemicals, it is necessary to take into account the spatial variability in above and below ground environments, which is inherent in almost every agricultural field.
This management is variously known as site-specific, precision, spatially-variable, soil-specific agriculture or farming. The concept of site-specific agriculture or precision farming is the use of local soil and crop parameters, to target inputs more accurately according to locally determined requirements of field crops. It is a developing technology that modifies existing techniques and incorporates new ones, to produce a new set of tools for the manager to use. The variation of crop and soil properties within the fields leads to attempts to understand these variations and to manage production accordingly.
Technological advance in other areas such as sensing and control systems is enabling precision agriculture systems to be implemented, though agronomic and decision support systems lag behind. Locating these differences within a large field is impossible without the access to positioning systems, and an increased use of electronics and computers in agriculture. The whole concept depends on the acquisition and interpretation of data on spatial variability although there are only a few existing sensing systems. Adequate utilisation of appropriate sensors will offer major opportunities for precise targeting of inputs.
The variability of soils, and hence the variability of crops, was probably known and used all the time by the farmers as it is still done by the Philippine rice farmers some of whom differentiated fertilizer inputs according to the visual impression of the crop. In the early part of the 20^{th} century, scholars were already studying variability in soil properties, such as nutrient status and organic matter levels (ROBINSON & LLOYD 1915). Researchers continued to report on soil and yield variability (FAIRFIELD SMITH 1938, REED & RIGNEY 1947, JAMES & DOW 1972, BURGHARDT 1984, SCHMIDT 1985, KNEIB 1985). The renown German MITSCHERLICH (1933, p 2) was the first to call the soil a “heterogeneous mixture”, detectable even on a small scale (MITSCHERLICH 1950a, p 14), thus biasing plot trials. He found soils being highly different at a distance of 10 m (MITSCHERLICH 1950b, p 297), and concluded the value of small scale maps “illusory” for practical farming at that time. The mechanisation of agriculture and the trend towards larger implements and fields led agriculture in the direction of large-scale farming.
In the last two decades, technological advances and the pressure of public opinion about environmental effects re-vitalised the idea of defining smaller management units, and applying agrochemicals based on the individual characteristics of those units, in the concept now generally referred to as precision farming or site-specific agriculture. The concept of precision farming is not a product of engineering technology, but rather has its foundations in the application of sound agronomic principles on an area-to-area, point-to-point basis. The practical implementation of precision farming concepts, however, is dependent on technological developments to provide, manage, and utilise the information on field heterogeneity.
Depending on the way of processing, these information can be managed either on an off-line basis, where “the input data ... are collected and held until a convenient later time for processing”, or on an on-line basis, where “the input data are processed as they become available” (WEBSTER 1999a). Based on the needed response time, on-line sensors are further called real-time sensors, “if response time is an important determination of correct functioning” (WEBSTER 1999b).
The practical implementation of site-specific agriculture, and therefore, the technological development is not limited to a specific research area. Furthermore, many research areas are interacting with each other on site-specific topics. Therefore, this state of art will also mention major works in the more important areas of site-specific agriculture.
Soil and yield were the first to come into the focus of precision farming research. For obtaining soil data and quantifying specific soil properties, two approaches are in use: traditional soil sampling, and the development of sensors for either remote sensing or machine-based sensing. Traditional soil sampling is highly accurate yet time-consuming (ALTMANN & HAASE 1984, DAHIYA et al. 1984, ARMSTRONG 1986, DOBERMANN 1994, DOBERMANN et al. 1994, DOBERMANN et al.1995, DOBERMANN et al.1997) which is inadequate for soil parameters that change rapidly both spatially and temporally, and hinders the impact in practical farming. Therefore are the technicians focussing on remote sensing of soil properties – which are mostly hindered by weather conditions (MORAN et al. 1998, DIKER & BAUSCH 1998), and machine-based sensors. Several machine-based soil sensors exist, such as a real-time nitrate sensor (ADSETT & ZOERB 1991, BIRRELL & HUMMEL 1997), automated soil sampling collection systems (MC GRATH et al. 1995, LÜTTICKEN 1998a, COLBURN 1998, HALE 2000), sensing of soil slope (GEBBERS & SCHMIDT 1999), sensing of soil organic matter content via reflectance (PAGE 1974, KRISHNAN et al.1980, GRIFFIS 1985, SUDDUTH et al. 1990, SHONK et al. 1991), estimating soil moisture (KANO et al. 1985), soil compaction (DOMSCH & WENDROTH 1997) and soil electromagnetic sensing (WILLIAMS & HOEY 1987, KACHANOSKI et al. 1988, RHOADES et al. 1989, SHEETS & HENDRICKX 1995). These approaches face the problem of changing weather conditions both in the season and between the years (LAMB et al. 1997) and a sometimes low correlation between yield and soil nutrient levels (BIRRELL et al. 1993), and soil parameters can not be used for a site-specific crop protection, since there is almost no relationship between the soil and leaf fungi infestation.
Crop yield and its within-field variation is an important input for site-specific decision making and is in itself an integrator of many varying crop and soil parameters, thus becoming a main focus for engineers to develop crop yield sensors integrated in harvesters, such as grain flow sensors (WAGNER & SCHROCK 1987, PETERSON et al. 1989, SEARCY 1989, PANG & ZOERB 1990, STAFFORD et al. 1991, VANSICHEN & DE BAERDEMAEKER 1991, BORGELT & SUDDUTH 1992, KLEMME et al. 1992, PFEIFFER et al. 1993, STOTT et al. 1993, IIDA et al. 1998), or sensors for determining silage yield (VANSICHEN & DE
Variable-rate technology for application control to differentiate crop inputs automatically while traversing a field has been in development for some time to control the amount of liquid and dry fertilizers, herbicides, seeds, and manure (CAHN 1995, OLIESLAGERS et al. 1995, GOENSE 1998, LÜTTICKEN 1998b, THIRION et al. 1998, SEYMOUR et al. 2000). Control of field sprayers was at first a mere on/off control and later became a variable rate control (FELTON & MC CLOY 1992, BALSARI & TAMAGNONE 1997, SCARR et al. 1997, VIERI & SPUGNOLI 1997). Without variable rate technology, an advance in site-specific agriculture is handicapped, because a decision to differentiate the application rate needs the techniques to apply these different rates.
The economic situation of precision farming is mainly unexplored. REETZ & FIXEN 1995 reported a net income of US-$ 43 per hectare, or a total of US-$ 2 798 for a 65 ha farm. And according to SCHMERLER & BASTEN 1999 ranges the economic benefit of site-specific management between + 15 EURO ha^{-1} and + 50 EURO ha^{-1}. These results enforce the development of sensors that are able to scan at low costs per hectare to leave the benefit with the farmers. Only low-cost sensors can bear this economic pressure for sensing temporal and spatial variability, or sensors whose measurements have a low dependency on time, and thus, are suitable for various management dates or even seasons. Real-time sensors can provide a sampling intensity several magnitudes higher than traditional methods, and therefore, can tolerate a much higher analysis error while providing greater overall accuracy in mapping variability. These sensors can potentially reduce costs while improving the accuracy of the data collection process. In site-specific application of nitrogen top-dressing several machine-based real-time sensors were developed using image vision (SINGH et al. 1996) or leaf reflectance technology (SOLIE et al. 1996, STONE et al. 1996, REUSCH 1997, STAFFORD & BOLAM 1998, GÜNTHER et al. 1999, NIELSEN et al. 1999, WOLLRING & REUSCH 1999).
Differentiated herbicide application rates can be derived from previously mapped weed information (BROWN et al. 1990, WARTENBERG 1996, NORDMEYER et al. 1997, WILES & SCHWEIZER 1998, SÖKEFELD et al. 1999), or through on-board weed sensing by locating green plant material on bare ground (HAGGAR et al. 1983, FELTON & MC CLOY 1992, MERRITT et al. 1994), and a sensor/nozzle unit developed by PATCHENInc., California (HANKS 1995). More sophisticated sensors are under development (BILLER et al. 1997, VRINDTS & DE BAERDEMAEKER 1997, BROULIK et al. 1998, FEYAERTS et al. 1998,
In site-specific differentiated application of growth-regulators, no on-line sensor so far is able to sense the crop parameters commonly used for applying site-specific differentiated amounts of these regulators, such as crop density. Only a few publications exist considering the application of growth-regulators according to soil maps and crop densities compiled by a German research group (SCHULZKE 1982, SCHÄDLICH et al. 1985, SCHÄDLICH et al. 1986, SCHULZKE & THIERE 1986, SCHULZKE et al. 1986). Just recently was the here presented pendulum-meter used by a research group to measure the lodging susceptibility of wheat (UDOH et al. 2000).
The potential of site-specific differentiated application of fungicides is still untouched due to the lack of sensors that are able to sense either the crop parameters, influencing growth of fungi, or sensing fungi on or in the plants directly. Only a double-step approach based on soil maps and then weather data, and later processed in decision support systems, is working up to date (SECHER et al. 1996a, AUDSLEY et al. 1997, SECHER et al. 1997, VOLK 1998, BJERRE 1999). Further practised is the traditional field sampling of fungi infestation or plant parameters (SECHER et al. 1995, SECHER et al. 1996b, KUTCHER et al. 1998). The exception is a study conducted to use remotely sensed digital imagery to evaluate Sclerotinia sclerotiorum infestations in soybeans (DUDKA et al. 1998), and a publication suggesting a spectral reflectance technique for disease diagnosis on cucumber leaves (SASAKI et al. 1998). Both methods are not working so far in cereals. All decision support systems for plant protection, such as PRO_PLANT^{®} in Germany or the Danish PC-PLANT PROTECTION^{®}, take crop density into account for their advice on the use of fungicides and growth-regulators, and this works usually on a field scale or even a regional scale. Therefore, a sensor for determining crop density is highly desirable, but so far such a sensor has not been reported.
As long as there is no way to sense crop density, other potentially useful plant parameters such as crop biomass have to be used. Determination of crop biomass by using radioactive radiation (KÜHN & SCHÄTZLER 1976, FREYTAG & JÄGER 1979, FREYTAG et al. 1983, UNGER & QUILITZSCH 1984) is not advisable. Remote sensing of crop properties is also possible, but mostly hindered by weather conditions and a narrow time schedule (ASRAR et al. 1985, LORD & DESJARDINS 1985, WILLIS et al. 1998, ZHANG et al. 1998). Additionally, estimation of crop biomass is possible by subjective visual assessment (CAMPBELL & ARNOLD 1973, PIGGOT & MORGAN 1985), through off-line point-to-point data measured by disk-meters or plate-meters (table 1), and capacitance-meters (CAMPBELL et al. 1962, BACK 1968, ANGELONE et al. 1980, VICKERY et al. 1980, MICHELL & LARGE 1983, THOMSON
Authors
Year
Crop / Predominant Plant
Object
Utilisation Type
POWELL 1974 grasses & herbage DM pasture CASTLE 1976 grasses DM pasture EARLE & MC GOWAN 1979
Lolium perenne
DM pasture BAKER et al. 1981 grasses & herbage DM meadow MICHELL & LARGE 1983
Lolium perenne
DM pasture FARIAS & THOMAS 1984
Lolium multiflorum
DM & FM meadow SHARROW 1984 grasses & herbage, and Avena sativa
FM pasture STOCKDALE 1984 grasses & herbage DM pasture GIBB & RIDOUT 1986
Lolium perenne & Trifolium repens
sward height pasture PALAZZO & LEE 1986
Festuca arundinacea & Lespedeza cuneata
DM meadow PIGGOT 1986 grass DM pasture SCRIVNER et al. 1986
Lolium multiflorum & Trifolium subterraneum
DM pasture FULKERSON & MICHELL 1987
Lolium perenne & Trifolium repens
DM pasture ILLIUS et al. 1987 grass sward height pasture KARL & NICHOLSON 1987 grass & herbage DM meadow PETERSON & HUSSEY 1987
Cynodon dactylon
DM pasture GIBB & RIDOUT 1988
Lolium perenne & Trifolium repens
sward height pasture L'HUILLIER & THOMSON 1988
Lolium perenne & Trifolium repens
FM pasture MARTINEZ et al. 1988
Cynodon dactylon
DM pasture BRYAN et al. 1989 grasses & herbage DM pasture CARLIER et al. 1989 grasses DM pasture & meadow GIBB et al. 1989
Lolium perenne & Trifolium repens
sward height pasture GREEN et al. 1989
Festuca arundinacea & Lolium perenne
DM pasture KRUMIN'SH 1989
Dactylis glomerata
DM meadow LACA et al. 1989 grass & herbage DM pasture MARTINEZ et al. 1989 grass DM pasture
Authors
Year
Crop / Predominant Plant
Object
Utilisation Type
MARTINEZ-EXPOSITO et al. 1989
Neonotonia wightii
DM pasture PIGGOT 1989 grass & herbage DM pasture GONZALEZ et al. 1990
Cynodon dactylon
DM pasture MOULD 1990 grasses DM pasture & meadow GOURLEY & MC GOWAN 1991
Lolium perenne & Trifolium repens
DM pasture HUTCHINGS 1991 grass sward height meadow AIKEN & BRANSBY 1992
Festuca arundinacea
FM pasture CRAWFORD 1992
Lolium multiflorum
DM meadow MOULD 1992 grass DM pasture GABRIELS & VAN DEN BERG 1993
Lolium perenne
DM pasture & meadow HATCH & TAINTON 1995 grass & herbage DM savannah MURPHY et al. 1995 grasses & herbage DM pasture VIRKAJÄRVI & MATILAINEN 1995
Phleum pratense
FM pasture BROCKETT 1996 grass DM biomass production REEVES et al. 1996
Pennisetum clandestinum
DM & FM pasture TEAGUE et al. 1996
Bothriochloa ischaemum
sward height pasture HARMONEY et al. 1997 grasses & herbage DM pasture RAYBURN&RAYBURN 1998 grasses & herbage DM pasture MOSIMAN et al. 1999 grasses & herbage DM pasture & meadow
Additional methods of biomass sensing were hand-held devices for red or infrared reflectance (DRAKE 1976, BUERKERT et al. 1995) and hand-held radiometers (PEARSON et al. 1976a, AASE & SIDDOWAY 1981, WALLER et al. 1981, HARDISKY et al. 1984, ASRAR et al. 1985), a crane-based microwave backscattering technique (WIGNERON et al. 1995), and leaf area indices LAI measurements (DOBERMANN & PAMPOLINO 1995, HARMONEY et al. 1997). Sensing of crop biomass was reported by remote sensing through radar (STEINGIESSER 1997, DEGUISE et al. 1998), computer image analysis (THAMM 1986, EVERS et al. 1987, GROSS et al. 1987, THAMM 1989), and infrared reflectance (PEARSON et al. 1976b, TUCKER et al. 1981). Machine-based biomass sensing was conducted by using visible and near infrared reflectance (TUCKER 1979, DUSEK et al. 1985, LORENZEN & JENSEN 1988, RICHTER 1988, JENSEN et al. 1990) and mechanical scanning (EHLERT & SCHMIDT 1996, EHLERT & JÜRSCHIK 1997, EHLERT 1998). Several researchers used LAI or NDVI measurements as a base for decision making in plant protection (CHRISTENSEN et al. 1997, PAICE et al. 1999). Unfortunately, most methods are not suitable for on-line sensing, and taking samples is time-consuming for providing application maps, and is cost-intensive when used on a small field scale. Of the two most promising on-line methods, the mechanical scanning and the NDVI-scanning, the second is a much more expensive measurement system than the first.
The objective of this work is to fill a gap of the need for non-destructive, site-specific, machine-based, on-line sensing of still standing crop biomass by optimizing the mechanical sensor developed by the Institute of Agricultural Engineering ATB (EHLERT & SCHMIDT 1996, EHLERT & JÜRSCHIK 1997, EHLERT 1998), as a tool for site-specific management in heterogeneous fields.
The focus of this work is the optimisation of the swinging pendulum biomass sensor for the cereal crops winter rye (Secale cereale L.), winter wheat (Triticum aestivum ssp. Vulgare) and irrigated rice (Oriza sativa L.) at the usual application timings for plant protection. These application dates are the timings at which management decisions such as fungicide treatments or spraying with growth-regulators will have to be decided for the site-specifically optimised plant protection. At these timings, different pendulum parameter settings of the mechanical sensor can be proved on the same strip of cereal crops with a high heterogeneity in crop biomass.
Accuracy and repeatability of the replicates have to be determined for the different possible parameter settings of the sensor. Therefore, the measurements will be repeated with the necessary number of replicates of the same setting and then validated.
The limits of the parameter settings, at which the measurements prove non-destructive, will be examined.
The accuracy of the site-specific biomass determination for the different sensor parameters will be clarified. Consequently, it will be examined to which degree the sensor measures dry mass and fresh mass of the crops. The ability of the sensor measurements to determine additional plant parameters such as crop height or crop density will also be clarified in several trials.
It is necessary to identify the factors, associated with a biasing effect on the biomass measurements or the regression equations of the measurements and the cut and weighed biomass. Several factors such as cultivar, season, management methods, and growth of the crop may bias the measurements, and therefore, will be detected and proved wrong or true.
The use of the sensor for site-specific differentiated plant protection will be shown in primary field trials, therefore, the potentials of site-specific plant protection according to crop biomass will be shown in one trial with a plant growth-regulator, and another trial with a fungicide treatment. The utilization of crop biomass as a decision base for plant protection will be discussed.
The measurement principle, first described by EHLERT & SCHMIDT (1996), is the basis for the advanced measurement principle described in this work.
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
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.
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
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.
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
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
[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]
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,
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
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}.
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.
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.
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.
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).
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.
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
30^{th} October 1997 AMILO 120 kg ha^{-1}
Fertilizing
25^{th}March 1998 21 / 21 / 21 N P K 90 kg ha^{-1} nitrogen
Growth-regulator
8^{th}May 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
12^{th} October 1998 BATIS 80 kg ha^{-1}
Fertilizing
31^{st}March 1999 Calcium-ammonium-nitrate 137 kg ha^{-1} nitrogen
Herbicide
2^{nd}May 1999 STARANE 0.7 l ha^{-1}
Fungicide*
20^{th}May 1999 JUWEL TOP 1.0 l ha^{-1}
Growth-regulator
20^{th}May 1999 MODDUS 0.4 l ha^{-1}
* site-specific application in some parts of the field as site-specific field trial
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
27^{th} December 1998 IR 64, 14 days old DAPOG seedlings 33 hills m^{-2},
Fertilizing
26^{th} December 1998 Urea 65 kg ha^{-1} nitrogen
Rat protection
15^{th}January 1999 Plastic fence
Weeding
28^{th}February 1999 By hand
2-3 plants per hill
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 8^{th} April and the 24^{th} May 1998, and 21 mm of precipitation between the 25^{th} April and the 31^{st} 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 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
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
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 m^{2} 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.
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 m^{2} 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 m^{2} 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 m^{2} 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 m^{2} 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 m^{2} 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
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
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 5^{th} replicate.
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 h_{P}, 0.1 m h_{A0}, 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.
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 h_{P}, 0.3 m h_{A0}, 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.
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 h_{P}, 0.1 m h_{A0}, 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.
The developed biomass sensor “Pendulum-Meter” works mechanically through a pendulum that is moved by forces created from cereal stems touching the pendulums cylindrical body. The combined forces of the stems push against the cylindrical body, moving it out of place in an angular motion with the aid of a support tube that is connected to a pivot point. The pivot is the focus point of the relationship between the unmoved pendulum and the deflected pendulum which is described by the angle of deviation α.
Taking measurements using a pivoted cylindrical body, moving horizontally through a standing cereal population (figure 8), the angle of deviation α is caused by the resultant force F_{R} which deviates the cylindrical body from the undeviated position B into the deviated position B’. The resultant force is itself determined by a combination of the following parameters: height of the pivot point h_{P}, length of the pendulum l_{P}, height of the undeviated cylindrical body h_{A0}, mass of the pendulum m _{P}, driving velocity v_{D}, mass of the stems m _{S} – including moments of inertia, bending moments of resistance of the stems R_{Mb}, friction force between pendulum and stems F_{F}, number of stems n_{S}, and plant height h_{Pl}.
Through keeping the technical parameters, height of pivot point, length of pendulum, mass of pendulum, and driving velocity within the field almost constant, therefore, the angle of deviation
It follows from this dynamic motion that the cylindrical body moves against the stems, thus forcing the pendulum to deviate from the original position with the angle zero into a position of angle α. In this position, the force the pendulum applied onto the stems is equal to the force the stems develop against the pendulum, while they are themselves moved out of place.
The resultant force F_{R} of the pendulum depends on the weight of the cylindrical body, and the length of the pendulum, and is measured initially at 90 degrees deviation, and then calculated for the measured angle according to the following mechanical background (figure 9):
The resultant force F_{R} in Newtons can be divided into its vertical F_{V} and horizontal F_{H} force components, calculated from the weight at 90° deviation F _{A}, using equation 12:
F
A
(N) =
m
(kg) · g (m s
^{-2}
)
[12]
F
R
_{
(N) =
F
A
(N) · sin
α
}
[13]
where F R is the resultant force of the pendulum at a specific angle of deviation, and F A is the force of the pendulum at 90° deviation of the pendulum. The vertical force F _{V} for a specific angle α is calculated by equation 14:
F
V
(N) =
F
A
(N) · sin
^{2}
α
[14]
where F V is the vertical force of the pendulum at a specific angle of deviation, and F A is the force of the pendulum at 90° deviation of the pendulum. The horizontal force F _{H} for a specific angle α is calculated by equation 15:
F
H
_{
(N) =
F
A
(N) · sin
α
· cos
α
}
[15]
where F H is the horizontal force of the pendulum at a specific angle of deviation, and F A is the force of the pendulum at 90° deviation of the pendulum.
According to standard engineering theory the resultant force F_{R} of the pendulum, working in the opposite direction, equals the force that the stems are applying against the cylindrical body. Thus equation 13 aids calculating the force of the sum of the stems that are working to deviate the pendulum.
The resultant force of the stems can be divided into three causes:
The bending moment of resistance of the stems
The mass moment of the stems
The friction between the stems and the pendulum
In mechanical theory a still standing cereal stem is considered a one-fixed end cylindrical hollow beam, a cantilever. Due to this closed-form solution, bending moment of resistance, mass moment of inertia and friction force can be calculated by standard mechanical equations.
While some of these factors can be calculated, such as the second moment of the area I, others can only be determined by material tests such as the elasticity of the material E. All calculations
Although in this work it is not possible to calculate bending moment of resistance and mass moment of inertia or friction due to a lack of data, such as the inner and outer diameter of the stems, the modulus of elasticity E, or the height of the gravity point of the stem, it is nevertheless good for explaining some of the results of the pendulum-meter measurements.
During measurements the most commonly seen bending type, the curved bending, is the one shown in figure 10 as type 2. This type was encountered at all growth-stages in paddy rice and winter wheat, and in winter rye at the growth-stages BBCH 32 to 59. After heading the bending types encountered in winter rye changed from type 2 to type 3, the u-formed bending. The rigid type 1, linear bending, was seen only on single standing plants after flowering with a low contact height of the cylindrical body.
A sensory device to bend cereal stems requires mechanical parts which make contact with the stems or plants. The pendulum-meter consists of three major parts thus forming a pendulum: a housing at the pivot point, a rigid tube, and a contact bar (figure 11). The contact bar is a rigid, hollow plastic tube with 5 cm outer diameter forming a cylindrical body of one meter length to engage the plant stems, which is arranged transverse to the measurement direction. The cylindrical body is the part of the pendulum receiving the forces created by the plant stems. A tube connects it with the pivot point, and its rigidity transfers the forces straight to the other end of the rigid tube where the deviation of the pendulum is measured by the potentiometer.
The pivot point arrangement consists of a metal housing (figure 12), containing two ball bearings and seal rings, and supports a stub shaft which forms a pivot axis. This pivot axis transmits the swinging motion from the rigid tube to the potentiometer. This potentiometer, an incremental encoder type RB from IFM Electronics, measures the angular motion as electrical variation between +5 and -5 Volts. The friction of the seal rings causes a slight dampening of the swinging movement of the pendulum, that can swing over an operating range of 200° between +110° and -90°. A pendulum length of 0.1 m was not possible due to the friction of the pivot point with the seal rings disturbing the swinging of the pendulum. Caused by different construction materials, the pendulum-meter used in rice had different weights, and hence a different vibration time, than
For the optimisation of measurements the following parameters of the pendulum-meter can be varied: the height of the pivot point h_{P}, the height of the cylindrical body h_{A0}, the mass of the pendulum m _{P} and the length of the pendulum l_{P}. Since the height of the pivot point and the height of the cylindrical body determine together the length of the pendulum, and the measurement takes place through physical contact with the plants, the focus of the optimisation trials is on the height of the pivot point, the height of the cylindrical body, and the mass of the pendulum. These three parameters can be related to plant parameters.
Firstly, the height of the pivot point is usually tested instead of the length of the pendulum, starting with heights of the pivot point considerably higher than the plant height, reducing the height in steps of 0.1 meters, with the exception of one parameter trial in winter rye 1998. Each step is a single parameter until the plants show marks of destruction.
The setting for the height of the cylindrical body h_{A0} takes place similarly. The lowest average plant height limits the maximum height of the cylindrical body, which changes mostly in steps of 0.1 meter, and the lowest setting height is 0.1 meter. The cylindrical body in all cases operates over a width of one meter.
In the optimisation tests the mass of the pendulum starts with the self weight, then added up to one kilogram, and increases in steps of 0.5 kg until the plants show marks of destructiveness. With regard to rice most parameter settings are tested with the self weight of the pendulum, since the rice plants show sometimes marks of destructiveness at even the 1 kg weight of the pendulum.
Length of the Pendulum (m)
0.200
0.300
0.400
0.500
0.600
0.700
Rice Pendulum (kg)
0.450 0.497 0.519 0.533 0.556 0.576
Wheat / Rye Pendulum (kg)
0.522 0.549 0.574 0.595 0.615 0.635
Length of the Pendulum (m)
0.800
0.900
1.000
1.100
1.200
1.300
Rice Pendulum (kg)
0.604 0.618 0.644 0.693 0.734 -
Wheat / Rye Pendulum (kg)
0.657 0.676 0.696 0.716 0.735 0.752
With the determined mass of the pendulum at a specific length, it is possible to calculate the resultant force at various angles that is necessary to deviate the pendulum. Using equation 12 and 13, the resultant force is calculated for a specific angle of the rice pendulum with a length of one meter and a self weight of 0.644 kg. Thus, it is possible to calculate the resultant forces for a wide range of angles (figure 13).
Figure 13 provides a good illustration of the relation between resultant force and angle, which is not linear but second degree polynomial. Nevertheless, the difference between goodness of fit for the linear and square formulas is small. The necessary amount of resultant force required to deviate the pendulum for one unit of angle increases the greater the deviation becomes.
Based on the measurement principle, there are several factors which are in standard mechanical theory considered either dependent on the mass or highly influenced by the mass of a body.
The mass moment of inertia is dependent on the mass of a body, as can be seen in equations 1 and 2, and also the acceleration is dependent on the mass of a body as shown in equation 3.
The bending moment of resistance is highly influenced by the second moment of the area I of the body as described by equation 4 and 5. The factor bending distance x depends upon the curvature of the stem, and is considered equal within a field. The second moment of the area I is a geometrical factor giving a cross section of the material of the stem and HITAKA 1968 reported a correlation coefficient between I and the fresh weight per unit length of 0.75. Young’s modulus of elasticity E was stated (NISHIYAMA 1986) to have a close correlation with the specific weight of the material. Several works (HITAKA 1968, GOWIN 1989, TOMASZ 1989) described a negative correlation between E and I, meaning an increase in I decreases the elasticity and vice versa. Several studies (HITAKA 1968, NISHIYAMA 1986) provided evidence that the bending rigidity E I has a correlation with I, when culm length and E are the same, and that the bending rigidity E I increases with E, when culm length and I are the same. MÜLLER 1988 suggested that bending rigidity E I is in direct proportion to the fourth power of the external diameter of the stem ,when E doesn’t change much. HITAKA 1968 found a good correlation between E and fresh weight per unit length. The bending rigidity E I, as the product of the modulus of elasticity E and the second moment of area I, showed according to HITAKA
HITAKA 1968 also found high correlation coefficients between E I and:
breaking strength of the 4^{th} internode of the culm without leaf sheath of 0.98,
breaking strength of the 4^{th} internode of the culm with leaf sheath of 0.91,
external culm diameter of 0.91,
cross section of 0.77,
thickness of culm wall of 0.64
and determined the relationship between bending rigidity of the stem and its fresh weight with a correlation coefficient of 0.84.
The sliding friction between the biomass sensor and the plants is not dependent on the mass of the plant, but on the friction coefficient between both surfaces. Additionally, dependencies of the gliding friction were associated with pressure (USREY et al. 1992) and moisture content of the crop (KUTZBACH 1989, HUISMAN 1978).
Due to the lack of data for the various factors in the equations, the changes of these factors within the test strip, and the summarised measurement of various different stems at once, a calculation of bending moment of resistance and of the mass moment of inertia is not possible. But a weighing of the participation of the factors on the measurement is possible. USREY et al. 1992 reported a friction coefficient of 0.306 between rice and polyethylene. For a vertical force of 1 Newton this gives a friction force of 0.3 Newton, or 30 % of the vertical force in general. Kinetic friction coefficients of 0.19, 0.23, and 0.31, were reported between winter wheat straw and steel (HUISMAN 1978), as well as friction coefficients of 0.2 to 0.4 between polyvinyl-chloride and Italian ryegrass (KUTZBACH 1989). An eventual change of the friction coefficient during crop growth is unexplored. Due to the square power of the height of the centre of gravity in equation 1 at the most growth-stages and the low masses above the centre of gravity at the early growth-stages, the mass moment of inertia is also considered of lower influence on the angle of deviation for the tested velocities.
As the main factor for the deviation of the pendulum is seen to be the bending moment of resistance of the stems, and most of the variables in the equations 1-7 are correlated or influenced by the mass of the stems. Not taken into account are the rotary induced forces onto
O’DOGHERTY et.al. 1989 used a rotary potentiometer for measuring E, and GROBLER & POTGIETER 1989 used a pendulum to measure fibre toughness in asparagus, while HITAKA 1968 used a top load at the panicle base to bend the rice stems. All three devices are in important parts similar to the pendulum-meter, as is the snap-test (MURPHY et al. 1958) which was found highly correlated with lodging resistance. Similarly tested GROBLER & POTGIETER 1989 fibre toughness of asparagus with a pendulum device. The widely used disk-meters and plate-meters utilise the bending moment of resistance of the stems as does the pendulum-meter, but they are lacking the mass moment of inertia effect of the stems, and instead of sliding friction they encounter adhesive friction. Since the bending moments were in both measurement devices the most important factors, the disk-meters and plate-meters can be considered highly similar.
The equations are also based on the ideal form of a cantilever, but the stems vary to different extents from this form, most notably are the nodes and the leaf sheaths, and E and I can only be used according to standard material science, when an elastic and isotropic material is provided. The stem can be considered elastic as long as it moves fully back into its former form, but only small layers of the stem can be judged as isotropic. Nevertheless, the use of these equations are common standard in determining the values of the stem, as long as it belongs to the type 2 in figure 6. Type 1 is a maximum rigid stem which also provides tensile forces onto the root. The equations are also not suitable for bending type 3 with maximum elasticity and low second moment of area, where the stress-strain relation is not linear.
The stiff cylindrical body, the rigid tube and a compact connection from tube to potentiometer are fundamental for a good transmittance of the forces of the stems against the cylindrical body into a corresponding angle of deviation. Every movement within the hardware produces noise in terms of biomass sensing. Also necessary is a robust construction of the hardware for a long-term field use.
The mentioned pendulum parameters have to be optimised with regard to plant parameters such as plant height, since the biomass sensor is a contact sensor, and hence, dependent on the contact with the plant. Despite the reported importance of the length of the pendulum l_{P} (EHLERT & SCHMIDT 1996), the height of the cylindrical body is the most critical pendulum parameter to be optimised, since this is the parameter in direct contact with the plants. The relationship between the angle of deviation and the resultant force is strictly curvilinear, but the linear regression would be sufficient for sensing the resultant force, or the biomass respectively.
Due to the measurement principle of recording angles, it is as well necessary to record the slope of the field, since the slope is already deviating the pendulum without having anything to measure. The slope sensor is screwed inside the cabinet (figure 14) containing the electronics. It is calibrated on a platform without inclination and measures the slope as a voltage reading. It is a AccuStar ^{®} ^{Electronic Clinometer from Lucas Control Systems Products, with an accuracy of 0.1 degrees for slopes of less than 10 degrees. The slope measurements are taken into account for the angle of deviation versus biomass relationship with 0.1 degree increments to correct the pendulum measurements. }
An incremental visual encoder senses the velocity of the carrier (figure 15) during pendulum measurements. It senses the velocity through counting the holes in the disc that is mounted on the sprocket of a back wheel.
Since it was constructed at the Institute of Agricultural Engineering ATB, its accuracy is compared to the timer of the laptop, and a standard deviation of less than 0.08 m s^{-1 }was
A mechanical trigger (figure 16) is used to indicate the separation of the plots in the field. Sticks separate the plots in the field, instead of using a Global Positioning System GPS. The metal finger of the trigger is pushed backwards by the stick separating the plots, thus is the other end of the finger pivoted away from the inductive sensor. Once the other end of the metal finger is out of the range of the inductive sensor, the trigger switches either from 0 to 5 volts or vice versa. A rubber band is pulling the finger back into place for the next switch.
A buffer of one second in the electronic device prevents a second switch when the finger is pulled back in its place. All measurements belonging to one switch of the trigger belong to the same field plot in the recorded files. This trigger, together with the rails, is used instead of a GPS system to locate the position of the pendulum-meter in driving direction. At a measurement frequency of 75 Hz and a velocity of 2.5 m s^{-1}, the accuracy of the trigger is about 3 cm.
While all four sensors measure and transmit data all the time, recording of the measurements starts with the first change of the trigger. The sensors – biomass sensor, slope sensor, speed sensor, and trigger – transmit the measurements as Volt signals to the electronic cabinet (figure 17). Every sensor has a different entry into the electronic cabinet.
In this way it is possible to observe the correctness of the measurements and to repeat them in case of errors. Figure 18 shows the view of the screen, as it is seen during the measurements. While the window of the other sensors is showing the correctness of the measurements, the window of the speed sensor is all important for control and needs immediate correction of the velocity of the carrier, because all the measurements should have the same constant speed to avoid biasing the measurements.
After finishing the measurement, the values for each sensor are saved in an ASCII file containing five columns – one column for each sensor and one for the timer of the lap-top – and rows of measurements. Each row is a measurement at a specific time. The number of rows per unit time depend on the measurement frequency, with a frequency of 75 Hz there are 75 rows in one second. 75 Hz is the usual measurement frequency for all the tests. Each run in the parameter
The above mentioned calculation of the reduction of 150 measurements per plot to one value –the average of the plot – is based on the assumption that the average of a plot is a sufficient description of all measurements taken in one plot. A reduction of the measurements is necessary for the correlation with the single value of the plot biomass. Nevertheless, other statistical methods such as the median of the angle of deviation or the average of the vector, may as well be worth to use as a reduction method. Table 5 gives the value per plot for the three reduction methods average of the angle of deviation, average of the vector (equation 9), and the median of
Irrigated Rice
Winter Wheat
Winter Rye
Plot
Average angle of deviation
Angle of mean vector
Median
Average angle of deviation
Angle of mean vector
Median
Average angle of deviation
Angle of mean vector
Median
(°)
(°)
(°)
(°)
(°)
(°)
(°)
(°)
(°)
1
13.413 13.419 14.000 35.307 35.303 35.892 53.467 53.472 53.964
2
16.664 16.657 15.824 54.281 54.262 54.135 51.039 51.036 51.090
3
12.437 12.431 11.872 67.377 67.377 68.121 53.169 53.171 53.702
4
12.389 12.389 12.176 60.665 60.667 60.824 59.789 59.790 59.972
5
9.780 9.780 10.351 69.848 69.849 69.338 51.443 51.444 51.874
6
10.714 10.715 10.351 65.202 65.202 65.689 8.294 8.118 5.898
7
9.640 9.640 9.439 66.985 66.985 66.905 4.007 4.008 4.330
8
9.034 9.034 9.135 68.107 68.104 67.513 28.511 28.500 26.273
9
8.282 8.282 7.919 59.955 59.957 61.432 41.665 41.659 40.250
10
6.052 6.052 6.094 52.937 52.948 54.135 37.894 37.902 38.812
11
5.085 5.085 4.878 49.742 49.741 49.878 36.428 36.437 37.767
12
2.273 2.273 1.838 52.306 52.309 51.703 31.765 31.768 31.759
BBCH 39
BBCH 39
BBCH 39
When using statistics the question of normal distribution of the measurement values is of basic meaning. Since all further statistics will be based on the 12 plot averages, and a test for normal distribution needs a large sample size, it was not possible to test those 12 plot values for normal distribution. Instead, the least square residuals of the linear and square regressions were plotted
Winter Wheat and Rice
Figure 20 shows the standardised least square residuals plotted against predicted values of the linear and square regressions in irrigated rice at the growth-stage BBCH 39. For both regressions the residuals fell between +0.06 and -0.06 which is a very narrow distribution about zero. In winter wheat the residuals fell between +0.3 and -0.3 for a parameter of 0.5 m h_{P}, 0.2 m h_{A0}, and 0.497 kg m _{P} The residuals of both regression models are randomly distributed and a trend is not detectable, even at that small scale, thus indicating that the variance is constant and homogeneous. The residuals show normality, as well as independence of the errors. The small scale indicates a high fit of both regression models. The residuals of the linear regression show sometimes an outlier for the value of the plot with the lowest biomass. The closeness of the median and the average of the angle of deviation in table 5 also points to a normal distribution of the measurements.
Figure 21 shows the standardised least square residuals plotted against the predicted values of the linear and square regressions in winter rye at the growth-stage BBCH 39, calculated for a pendulum setting of 0.5 m h_{P}, 0.2 m h_{A0}, and 1 kg m _{P}. For both regressions the residuals fell between +1 and -1 which is a wider distribution about zero than encountered in rice and winter wheat but still in the limits between +2 and -2. The residuals of the square regression model were randomly distributed. A trend is not visible for this model, thus indicating that the variance is constant and homogeneous. The residuals of the square regression show normality, as well as independence of the errors. The scale indicates a lower fit of the regression model in winter rye. The residuals of the linear regression didn’t show normality, though they are still between the limits of +2 and -2. The variance of the residuals of the linear regression show a strong relation between the least square and the predicted values, but it is not linear. The values need a weighing or a preliminary transformation, to recalculate this error. Without transformation the model is inadequate. But for reasons of comparison with winter wheat and rice, the linear regression for winter rye is not excluded.
The slope sensor is necessary to correct the bias of the slope within the field. Without slope sensor the slope of the field is deviating the biomass sensor pendulum-meter without having touched a single stem. The error would be the slope of the terrain in degrees, hence in a plain field with almost no slopes the biasing effect would be very small, and a slope sensor wouldn’t be needed. Nevertheless, for the vast number of fields with slopes in some parts, the slope sensor has to be an integrated part of the biomass sensing of the pendulum-meter. The slope sensor used here is of sufficient accuracy to correct the biomass sensor, and the unevenness of the soil surface, such as holes and furrows, can be diminished using the average of the slope sensor.
The speed sensor is also an integrated part of the measurement system due to the dynamic measurement principle. The sensor is important to keep the speed of the carrier constant while measuring a crop. The accuracy of the speed sensor is sufficient to control the speed during measurement, and to clarify the speed versus angle of deviation relationship. In some cases the visual encoder can be blocked by leaves sliding in between the small gap and thus stopping the counting of the disc-holes for that time.
The trigger is with 3 cm highly accurate to record position in moving direction, and together with the rails that prevent a sideways movement of more than 1 cm, they minimise a biasing of the location to a negligible factor. The exact positioning is the crucial point to pair the measurement data with the corresponding stems. The use of a GPS system was not possible because the vibration of the carrier prevented the necessary contact with the satellites.
To reduce the original measurements, which were usually recorded with a frequency of 75 Hz, to a number of values that can be handled, all three tested methods of data reduction were suitable. Though the reduction to an average of the angle of deviation or an average of the vector for one plot resulted in almost the same values, the median was not differing much.
The plotted residuals show no biasing influence of whatever possible cause for both the linear regression and the square regression in rice and winter wheat. In winter rye the plotted residual show a difference between the square regression and the linear regression. While the residuals of the square regression don’t show a bias or a foreign influence, the residuals of the linear regression show a square influence. For that reason in winter rye only the square regression can be used. This may not be true for other measurements in winter rye since at the tested winter rye sites the biomass per square meter is itself not linearly distributed but declines strongly with higher biomass. For reasons of comparison with rice and winter wheat, the linear regression will be presented as well.
The accuracy of repeat or replicate of a sensor is usually described by the standard deviation and the coefficient of variation of the repeats.
For the parameter optimisation trials each setting is tested with five repeats. For each plot of the test strip the average of the angle, their respective standard deviation SD, and coefficient of variation CV is calculated. The averages of the angle show large differences in these 12 plots (Table 6), which is important to correlate the measurement data with fresh mass, or dry mass respectively. The measurements were obtained in winter rye at BBCH 39 with a setting of 0.5 m h_{P}, 0.2 m h_{A0}, 1.0 kg m _{P}, 75 Hz frequency, and 2.5 m s^{-1} carrier velocity.
Number
Average
Standard
Coefficient
Fresh Mass
Dry Mass
1
2
3
4
5
(°)
(°)
(%)
(kg m
^{-}
²)
(kg m
^{-}
²)
Plot 1
53.0 53.3 52.5 52.8 53.5 53.0 0.49 0.73 1.412 0.208
Plot 2
50.8 51.0 51.0 51.5 51.0 51.1 0.25 0.49 1.304 0.189
Plot 3
52.9 53.3 52.9 53.3 53.2 53.1 0.20 0.38 1.596 0.228
Plot 4
59.7 59.5 59.5 59.7 59.8 59.7 0.12 0.21 1.792 0.261
Plot 5
51.4 51.3 51.1 52.1 51.4 51.5 0.36 0.69 1.320 0.193
Plot 6
9.7 9.2 8.6 9.6 8.3 9.1 0.60 6.65 0.232 0.038
Plot 7
3.5 4.2 3.1 4.1 4.0 3.8 0.45 11.83 0.192 0.031
Plot 8
29.3 28.8 28.8 29.2 28.5 28.9 0.31 1.06 0.740 0.119
Plot 9
42.1 42.0 42.0 42.3 41.7 42.0 0.24 0.56 1.092 0.164
Plot 10
38.1 38.2 37.9 37.9 37.9 38.0 0.14 0.38 0.920 0.136
Plot 11
36.9 36.2 36.2 36.6 36.4 36.5 0.29 0.80 0.816 0.126
Plot 12
31.8 32.4 31.8 31.5 31.8 31.9 0.32 1.01 0.732 0.110
Average
38.2
0.31
0.80
1.012
0.150
of Repeat
of Angle
Deviation
of Variation
To reduce this influence of the biomass itself and optimising the different parameter settings in terms of repeatability, the relevant standard deviation and coefficient of variation for the parameter optimisation trials is calculated as the average over all 12 plots.
Due to the fact that the results of all parameter optimisation trials are similar, regarding standard deviation and coefficient of variation, one trial for each crop is vicariously described for all trials. Full records for all trials are in the appendix.
In winter rye at BBCH 39, the standard deviations of all the various parameter settings show only little differences (table 7). The standard deviations are generally very low, the mean of 12 plots is always less than 1.1 degree, for the most settings even lower than 0.5 degree. The minimum standard deviation of the 12 plots is similar, though still slightly lower than the mean. Nevertheless the maximum standard deviation, encountered in the 12 plots, show considerable differences between the settings, although still low with values of less than 2.2 degrees.
The coefficients of variation of the tested parameter settings show larger differences between the settings than the standard deviation. The values of the coefficients of variation for all settings except one are better than 5 %, for many even better than 2.5 %. An effect of the parameter mass of the pendulum m _{P} tends to increase the coefficient of variation, although it is obviously caused by the decrease in angle. In general, a low angle means a high coefficient of variation.
Some settings show zero or negative values for the minimum plot angle. Although a negative biomass is not possible, a negative measurement can occur at certain conditions. A high mass of pendulum provided, a plot with a low biomass can be measured as negative, if there is a high biomass with a high deviation in the preceding plot.
Pendulum
Standard
Coefficient
Angle of
h
_{P
}
(m)
h
_{A0
}
(m)
m
_{P
}
(kg)
mean
min.
max.
mean
mean
min.
max. 1.100 0.200 0.676 0.35 0.18 0.66 1.20 29 6 43 1.100 0.200 1.000 0.30 0.13 0.49 1.16 26 3 40 1.100 0.200 2.000 1.11 0.43 2.13 5.12 22 2 35 1.100 0.200 3.000 0.43 0.17 1.10 2.42 18 1 31 1.100 0.300 0.657 0.56 0.33 1.09 2.70 21 1 37 1.100 0.300 1.000 0.32 0.14 0.47 1.83 17 0 33 1.100 0.300 2.000 0.37 0.21 0.70 3.05 12 -2 27 1.100 0.300 3.000 0.39 0.19 0.80 4.22 9 -1 23 0.800 0.200 0.615 0.38 0.23 0.76 1.10 34 6 51 0.800 0.200 1.000 0.35 0.15 0.66 1.20 29 2 46 0.800 0.200 2.000 0.40 0.12 0.69 1.84 22 -1 37 0.800 0.200 3.000 0.36 0.14 0.79 2.09 17 -2 32 0.800 0.300 0.595 0.37 0.16 0.70 1.83 20 -2 40 0.800 0.300 1.000 0.37 0.09 0.59 1.47 25 -1 45 0.800 0.300 2.000 0.47 0.28 0.75 3.56 13 -2 30 0.800 0.300 3.000 0.42 0.23 0.60 4.41 9 -2 24 0.500 0.200 0.549 0.27 0.14 0.40 0.58 46 8 68 0.500 0.200 1.000 0.31 0.12 0.60 0.80 38 4 60 0.500 0.200 2.000 0.44 0.27 0.64 1.62 27 1 48 0.500 0.200 3.000 0.51 0.19 0.88 2.48 21 0 39 0.500 0.300 0.522 0.48 0.20 1.04 1.36 35 1 62 0.500 0.300 1.000 0.55 0.26 1.09 2.13 26 0 50 0.500 0.300 2.000 0.41 0.19 0.66 2.58 16 -1 36 0.500 0.300 3.000 0.44 0.25 0.61 3.86 11 -1 28 0.400 0.200 0.522 0.44 0.19 0.73 0.81 54 11 76 0.400 0.200 1.000 0.57 0.24 0.82 1.31 43 5 66 0.400 0.200 2.000 0.71 0.40 1.11 2.32 31 2 53 0.400 0.200 3.000 0.74 0.17 1.37 3.14 23 1 43
Parameter
Deviation
of Variation
Deviation
(°)
(°)
(°)
(%)
(°)
(°)
(°)
With regard to winter wheat at BBCH 39, the standard deviations of all the various parameter settings show only little differences (table 8). The standard deviations are generally very low, the mean of 12 plots is always less than 0.52°, and in the most cases, even lower than 0.3°.
Pendulum
Standard
Coefficient
Angle of
h
_{P
}
(m)
h
_{A0
}
(m)
m
_{P
}
(kg)
mean
min.
max.
mean
mean
min.
max. 0.400 0.200 1.000 8.42 7.44 9.31 13.33 63 36 76 0.500 0.200 1.000 0.19 0.07 0.53 0.32 58 35 69 0.500 0.300 1.000 0.28 0.13 0.69 0.58 48 19 63 0.600 0.200 1.000 0.24 0.06 0.61 0.45 53 32 62 0.600 0.300 1.000 0.25 0.07 0.49 0.58 42 17 55 0.700 0.200 1.000 0.13 0.06 0.21 0.28 47 28 55 0.700 0.300 1.000 0.23 0.06 0.76 0.62 37 14 48 0.800 0.100 1.000 0.16 0.10 0.25 0.32 51 37 57 0.800 0.150 1.000 0.14 0.05 0.26 0.28 49 33 55 0.800 0.200 0.615 0.21 0.09 0.36 0.42 49 33 56 0.800 0.200 1.000 0.14 0.10 0.20 0.31 45 28 52 0.800 0.200 1.500 0.26 0.07 0.68 0.63 41 23 49 0.800 0.200 2.000 0.22 0.06 0.45 0.58 39 20 47 0.800 0.250 1.000 0.16 0.04 0.22 0.40 41 21 49 0.800 0.300 1.000 0.34 0.09 1.08 0.96 36 15 45 0.800 0.350 1.000 0.25 0.05 0.80 0.84 30 8 41 0.800 0.400 1.000 0.43 0.13 1.46 1.94 22 2 34 0.800 0.450 1.000 0.48 0.20 1.58 3.36 14 3 27 0.800 0.500 1.000 0.52 0.12 1.01 7.62 7 1 16 0.900 0.200 1.000 0.13 0.04 0.28 0.31 41 24 47 1.000 0.200 1.000 0.14 0.06 0.27 0.35 39 24 45 1.100 0.100 0.696 0.12 0.05 0.21 0.25 47 36 51 1.100 0.200 1.000 0.16 0.08 0.37 0.43 37 24 43 1.100 0.300 1.000 0.28 0.07 1.00 0.97 29 11 36 1.200 0.200 1.000 0.19 0.12 0.35 0.49 38 25 43
Parameter
Deviation
of Variation
Deviation
(°)
(°)
(°)
(%)
(°)
(°)
(°)
The minimum standard deviation of the 12 plots is similar, though still slightly lower than the mean. The maximum standard deviation, encountered in the 12 plots, show differences between the settings, with values of less than 1.6 degrees. Trends in the standard deviation, caused by the pendulum parameters are not recognisable. One extreme outlying value is the setting of h_{P} 0.4 m, h_{A0} 0.2 m, m _{P} 1.0 kg, which shows a standard deviation of 8 degrees. The standard deviation is slightly lower for all settings in winter wheat than in winter rye.
The coefficients of variation of the tested parameter settings show similar results for the settings as the standard deviation. The values of the coefficients of variation for a wide range of settings
None of the settings show zero or negative values for the minimum plot angle. The angles most often range between 30°and 70°, and the plant height is not differing as much as in winter rye.
In irrigated rice at BBCH 39 – 42 DAT–, the standard deviations of all the various parameter settings show little differences (table 9). The standard deviations are, in general, very low, the mean of 12 plots is always less than 1.1 degree, and for a wide range of settings even less than 0.4 degree. The minimum standard deviation of the 12 plots is similar, though slightly lower than the mean standard deviation.
Pendulum
Standard
Coefficient
Angle of
h
_{P}
h
_{A0}
m
_{P}
mean
min.
max.
mean
mean
min.
max.
0.300 0.100 0.450 0.21 0.08 0.34 0.60 35 9 47 0.400 0.100 0.497 0.36 0.13 0.62 1.22 29 8 40 0.400 0.200 0.450 0.63 0.32 1.07 5.84 11 3 19 0.500 0.100 0.519 0.29 0.15 0.41 1.12 26 9 36 0.500 0.100 1.000 0.40 0.27 0.85 2.17 18 3 29 0.500 0.100 1.500 0.30 0.11 0.68 2.12 14 1 23 0.500 0.100 2.000 0.29 0.13 0.48 2.58 11 0 19 0.500 0.200 0.497 0.55 0.23 0.96 5.90 9 2 16 0.500 0.300 0.450 1.10 0.43 1.77 34.67 3 2 4 0.600 0.100 0.533 0.31 0.10 0.88 1.34 23 9 31 0.700 0.100 0.556 0.32 0.15 0.79 1.50 21 8 28 0.800 0.100 0.576 0.21 0.09 0.38 1.02 20 7 26 0.800 0.200 0.556 0.58 0.36 1.08 7.77 7 1 12 0.800 0.300 0.533 0.90 0.16 2.47 34.49 3 1 5 0.900 0.100 0.604 0.28 0.15 0.60 1.46 19 7 25 1.000 0.100 0.618 0.27 0.12 0.45 1.51 18 6 23
Parameter
Deviation
of Variation
Deviation
(m)
(m)
(kg)
(°)
(°)
(°)
(%)
(°)
(°)
(°)
The coefficients of variation of the tested parameter settings show the same trend between the settings as the standard deviation. The values of the coefficients of variation for all settings with a height of the cylindrical body of 0.1 meters are better than 2.6 %. The values of the coefficients of variation triple to 8 % for a height of the cylindrical body of 0.2 m, and jump to 35 % for a height of the cylindrical body of 0.3 m. As in winter rye and winter wheat, a low angle means a high coefficient of variation in irrigated rice as well.
Of the three crops winter wheat, winter rye and irrigated rice, the maximum standard deviation is the lowest in rice, followed by winter wheat, while winter rye has the highest maximum standard deviation. Regarding the variation in plant height, the order is the same: in irrigated rice plant height is the factor of the least variation of 5 % in this work, followed by winter wheat of around 100 %, while winter rye has the largest variation of these three crops in the factor plant height of 300 % encountered in this work.
The standard deviation and the coefficient of variation are different for the twelve plots with the highest values compared to the plots with the lowest biomass. But the differences in the standard deviation between the plots are small due to the, in general, very low standard deviation. The coefficient of variation CV shows a higher influence of the biomass, because the average angle of deviation of the biomass sensor is much lower in plots with a low amount of crop biomass, hence the coefficient of variation increases. In the plots with the low angles of deviation the high CV is not weighing too much, because in these plots there was almost no biomass to be harvested.
The parameter optimisation trials resulted in general in standard deviations that are very low for agricultural or biological materials. In most tested parameter settings SD was lower than 0.5°, and they were more or less the same in winter rye, winter wheat, and irrigated rice, and for all growth-stages and pendulum parameters. These values are lower than the ones reported by EHLERT 1998 due to the exact replication of the measurement through trigger and rails. The
The coefficient of variation shows values of mostly less than 3 % for all crops and growth-stages and pendulum parameters which are very low CVs for biological materials. Due to the dependence of CV on the average angle is the coefficient of variation low with high biomass and vice versa, which is supported by the results of VIRKAJÄRVI & MATILAINEN 1995 for a disk-meter biomass relationship. In winter rye some parameters show small negative values at the plots with a very low biomass, but the importance is negligible because in theses sites there is in this case no sufficient harvestable amount of biomass. The extreme outlier for one parameter in winter wheat can be considered a measurement error because the SD is about 10 times as high as all other standard deviations.
Only the height of the cylindrical body is of importance which has to properly touch the stems also in areas with low plant height. But the height of the cylindrical body is in this case not an absolute factor but a relative to the plant height acting factor. The other pendulum parameters show little influence on CV. With regard to standard deviation and coefficient of variation, the height of the cylindrical body is the only parameter that can be optimised in relation to the plant height in such a way that it is in proper touch with the stems and not just with the leaves.
For the disk-meters and plate-meters, which are the devices principally closest to the pendulum-meter, most research has shown a wide range of values for standard deviation and coefficient of variation. The CV of them was mostly higher than 20 % (BRYAN et al. 1989, MOULD 1990, GONZALEZ et al. 1990, GABRIELS & VAN DEN BERG 1993), only MOULD 1990 found a CV of 3–10 % for ryegrass, and STOCKDALE 1984 reported a CV of 10–22 %. HITAKA 1968 reported in rice coefficients of variation of 0.21 % for the area of cross section, 0.25 % for the modulus of elasticity, and 0.30 % for the bending rigidity of stems, hence low values for the central factors in the measurement principle of the pendulum-meter.
Of the two approaches, that are describing the quality of a sensor, one is the accuracy of repeat, and the other one is the degree, by which a desired factor can be described through a measured factor. Therefore, the goodness of fit is the second approach to describe the quality of the biomass sensor.
For the parameter optimisation trials, the twelve plot averages of the angle of deviation are correlated with their corresponding fresh and dry plant mass values. Due to the curvilinear relationship between the angle of deviation and the force needed to deviate the pendulum, for every setting a linear and a square (second degree polynomial) goodness of fit is determined. In addition, the standard error of estimate is calculated for every linear goodness of fit, and also for the square goodness of fit, if the latter differs considerably from the first. Due to the fact that the results of all parameter optimisation trials are similar, regarding the goodness of fit, one exemplified growth-stage of each crop is vicariously described for all trials. Full records for all parameter optimisation trials are in the appendix.
Winter Rye
In winter rye at growth-stage BBCH 59, the linear goodness’ of fit R^{2}s between angle of deviation and fresh plant mass, show only small differences between the tested parameter settings (table 10). The linear goodness’ of fit between angle and fresh plant mass range between 0.84 and 0.94, all of them with a significance higher than 0.99. The linear goodness’ of fit increase slightly with an increase in the height of the cylindrical body h_{A0} from 0.3 m to 0.5 m, though an optimum is not reached in this trial. The height of the pivot point doesn’t show tendencies, and increasing the mass of the pendulum increases linear goodness’ of fit in most cases. Because of the unknown limits at which destruction of the plants occur, only a small range of the parameter settings were tested in the first parameter trials, and the range of the tested parameters was expanded, the better the limits of non-destructiveness of the measurements were known. The actual height of cylindrical body, at which the decrease in the linear goodness’ of fit R^{2}s starts, as in rice and winter wheat, is not visible. Also other trials don’t show this optimum, because the uppermost height of the cylindrical body is the lowest plant height in the plots. Thus, with one or two plots of very low plant height in the test strip, the optimum height of the cylindrical body is not reached as in the other plots with double or triple plant heights. The linear R^{2} for the angle-dry plant mass relationship is the same as for the fresh plant mass.
Parameters
Fresh Mass
Dry Mass
Angle
h
_{P}
(m)
h
_{A0}
(m)
m
_{P}
(kg)
linear
R
^{2}
linear
SE
square
R
^{2}
square
SE
linear
R
^{2}
linear
SE
square
R
^{2}
square
SE
mean
(°)
min.
(°)
max.
(°)
0.500 0.200 0.549 0.85** 9.8 0.98** 3.6 0.80** 11.0 0.98** 3.7 58 5 80 0.500 0.200 3.000 0.94** 4.3 0.99** 1.9 0.91** 5.3 0.99** 1.7 30 0 50 0.500 0.300 0.522 0.89** 9.0 0.98** 3.9 0.85** 10.5 0.98** 3.8 51 2 78 0.600 0.200 0.574 0.84** 9.2 0.98** 3.4 0.80** 10.3 0.98** 3.5 54 5 74 0.600 0.200 3.000 0.92** 4.6 0.99** 1.8 0.89** 5.5 0.99** 1.8 30 -1 49 0.600 0.300 0.549 0.87** 9.0 0.98** 4.0 0.83** 10.2 0.98** 3.8 48 0 72 0.700 0.200 0.595 0.85** 8.2 0.98** 3.1 0.81** 9.3 0.98** 3.2 50 6 69 0.700 0.200 3.000 0.91** 4.8 0.99** 1.9 0.88** 5.6 0.99** 1.9 30 -1 47 0.700 0.300 0.574 0.89** 7.7 0.98** 3.7 0.85** 8.9 0.98** 3.4 42 1 65 0.800 0.200 0.615 0.84** 7.9 0.98** 3.2 0.80** 8.8 0.98** 3.2 48 6 66 0.800 0.200 3.000 0.92** 4.6 0.98** 2.3 0.88** 5.4 0.98** 2.2 29 1 46 0.800 0.300 0.595 0.88** 7.2 0.97** 3.7 0.85** 8.3 0.98** 3.3 41 2 62 0.900 0.200 0.635 0.86** 7.1 0.98** 2.8 0.82** 8.0 0.98** 2.8 44 5 61 0.900 0.200 3.000 0.92** 4.1 0.98** 2.4 0.89** 4.9 0.98** 2.3 27 2 43 0.900 0.300 0.615 0.88** 6.7 0.97** 3.6 0.85** 7.7 0.97** 3.3 38 3 57 1.000 0.200 0.657 0.86** 6.5 0.98** 2.6 0.82** 7.3 0.98** 2.5 42 6 57 1.000 0.200 3.000 0.92** 3.9 0.98** 2.3 0.89** 4.6 0.98** 2.2 26 2 41 1.000 0.300 0.635 0.89** 6.1 0.96** 3.6 0.85** 7.0 0.97** 3.4 36 4 54 1.100 0.200 0.676 0.87** 6.0 0.98** 2.6 0.83** 6.9 0.98** 2.5 38 4 53 1.100 0.200 3.000 0.92** 3.7 0.97** 2.3 0.89** 4.4 0.98** 2.1 23 1 38 1.100 0.300 0.657 0.89** 5.5 0.96** 3.4 0.86** 6.4 0.97** 3.2 32 4 49 1.400 0.200 0.735 0.86** 5.4 0.98** 2.3 0.82** 6.1 0.98** 2.3 34 4 47 1.400 0.200 3.000 0.91** 3.5 0.98** 1.8 0.87** 4.2 0.98** 1.7 23 3 35 1.400 0.300 0.716 0.89** 4.7 0.97** 2.5 0.85** 5.5 0.97** 2.5 29 3 43 ** Significance > 0.99 * Significance > 0.95 † not significant
Due to the considerable differences between the linear and square goodness’ of fit, the standard error of estimate is calculated for both of them. The standard error of estimate, calculated for linear goodness of fit, is rather high for winter rye at BBCH 59, with values ranging between 3.5 and 9.8 for fresh plant mass, and similarly for dry plant mass. The standard error of estimate is much higher in winter rye than in irrigated rice and winter wheat, without having larger angles of deviation. There is a tendency of increasing standard errors of estimate when the angle of deviation increases. For average angles of 30° the standard error of estimate is around 5, while for average angles of 50° there is a standard error of estimate of 9. There is as well a strong incidence that the mass of the pendulum lowers the standard error of estimate: with a pendulum mass of 0.6 or 0.7 kg the standard error of estimate is about twice as high as for a mass of 3 kg. A similar tendency is visible for the height of the pivot point. If the pivot point rises from 0.5 m to 1.4 m, the standard error of estimate decreases from 9.8 to 5.4. The standard error of estimate for the square goodness of fit is much smaller with values ranging between 1.8 and 4.0 for fresh plant mass, and similar values for dry plant mass. The standard error of estimate for the square goodness of fit is in the range of the values encountered in irrigated rice and winter wheat for the linear goodness of fit. Thus showing, that the square formula for the relationship between biomass and angle has not only the higher R^{2}, but also a much lower standard error of estimate.
Winter Wheat
In winter wheat at the growth-stage BBCH 39, the linear goodness’ of fit R^{2}s between the angle of deviation and fresh plant mass, calculated for the tested parameter settings, show only little differences (table 11). The linear R^{2}s between angle and fresh plant mass range between 0.80 and 0.92, all of them with a significance higher than 0.99. The linear goodness’ of fit decrease slightly with an increase in the height of cylindrical body h_{A0} to 0.5 m, though by far not as sharply as in irrigated rice. The linear goodness’ of fit compared to the pendulum’s parameter settings show a slight tendency for the height of cylindrical body h_{A0. }By increasing the height of the cylindrical body from 0.1 m to 0.3 m, the linear values of R^{2} increase as well.
Parameters
Fresh Mass
Dry Mass
Angle
h
_{P}
h
_{A0}
m
_{P}
linear
linear
square
linear
linear
square
mean
min.
max.
0.400 0.200 1.000 0.90** 3.9 0.95** 0.91** 3.8 0.94** 63 36 76 0.500 0.200 1.000 0.88** 3.6 0.93** 0.89** 3.6 0.92** 58 35 69 0.500 0.300 1.000 0.91** 4.2 0.94** 0.91** 4.1 0.93** 48 19 63 0.600 0.200 1.000 0.89** 3.1 0.93** 0.89** 3.1 0.92** 53 32 62 0.600 0.300 1.000 0.90** 3.7 0.93** 0.90** 3.7 0.92** 42 17 55 0.700 0.200 1.000 0.87** 2.9 0.92** 0.87** 2.9 0.91** 47 28 55 0.700 0.300 1.000 0.89** 3.4 0.92** 0.89** 3.4 0.91** 37 14 48 0.800 0.100 1.000 0.86** 2.2 0.92** 0.86** 2.2 0.91** 51 37 57 0.800 0.150 1.000 0.86** 2.4 0.92** 0.86** 2.4 0.91** 49 33 55 0.800 0.200 0.615 0.86** 2.6 0.91** 0.85** 2.6 0.90** 49 33 56 0.800 0.200 1.000 0.87** 2.7 0.92** 0.86** 2.7 0.91** 45 28 52 0.800 0.200 1.500 0.86** 2.8 0.93** 0.86** 2.8 0.91** 41 23 49 0.800 0.200 2.000 0.86** 2.9 0.93** 0.86** 2.9 0.92** 39 20 47 0.800 0.250 1.000 0.86** 3.0 0.92** 0.86** 3.0 0.91** 41 21 49 0.800 0.300 1.000 0.87** 3.2 0.92** 0.87** 3.2 0.91** 36 15 45 0.800 0.350 1.000 0.89** 3.3 0.92** 0.89** 3.3 0.91** 30 8 41 0.800 0.400 1.000 0.92** 2.9 0.92** 0.91** 3.0 0.91** 22 2 34 0.800 0.450 1.000 0.88** 3.0 0.90** 0.86** 3.2 0.90** 14 3 27 0.800 0.500 1.000 0.81** 2.2 0.88** 0.78** 2.3 0.86** 7 1 16 0.900 0.200 1.000 0.85** 2.6 0.91** 0.85** 2.6 0.90** 41 24 47 1.000 0.200 1.000 0.83** 2.5 0.91** 0.84** 2.5 0.90** 39 24 45 1.100 0.100 0.696 0.81** 1.9 0.89** 0.81** 1.9 0.88** 47 36 51 1.100 0.200 1.000 0.83** 2.3 0.91** 0.83** 2.3 0.89** 37 24 43 1.100 0.300 1.000 0.84** 2.9 0.90** 0.83** 2.9 0.89** 29 11 36 1.200 0.200 1.000 0.80** 2.3 0.89** 0.80** 2.3 0.88** 38 25 43 ** Significance > 0.99 * Significance > 0.95 † not significant
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
(°)
(°)
The square goodness’ of fit between angle of deviation and fresh plant mass show only little differences, regarding the parameter settings. The square goodness’ of fit range between 0.88 and 0.95, all of them with a significance higher than 0.99. The square R^{2}s compared to the pendulum’s settings show no tendency relative to the height of the cylindrical body h_{A0}, and neither for the other two parameters_{The square goodness’ of fit for the angle-dry plant mass relationship perform equally well for the fresh plant mass. There is a slight tendency towards a better determination of plant mass through the square R2s than through the linear R2s.}
Due to the similarity of linear and square goodness of fit, the standard error of estimate is calculated only for the linear goodness of fit. The standard error of estimate, calculated for the tested parameter settings, is rather low for winter wheat at BBCH 39, with values ranging between 1.9 and 4.2 for fresh plant mass, and similarly for dry plant mass. The standard error of estimate is higher in winter wheat than in irrigated rice, but the range is smaller in winter wheat, thus preventing to show tendencies influenced by the parameter settings or the angle of deviation.
Rice
In irrigated rice at the growth-stage BBCH 39, the linear goodness’ of fit between angle of deviation and fresh plant mass, calculated for a wide range of parameter settings, show only little differences (table 12). The linear R^{2}s^{between angle and fresh plant mass range between 0.88 and 0.97, all of them with a significance higher than 0.99. The linear R2s drop sharply for the two settings with a height of cylindrical body hA0 of 0.3 m, showing a linear R2 of 0.62 and 0.24 respectively. The linear R2s compared to the pendulum’s settings show a strong trend for the height of cylindrical body hA0. By increasing the height of the cylindrical body, the linear R2s decrease. The actual height of the cylindrical body, at which the decrease in the linear goodness’ of fit starts, is dependent on the plant height. Once the cylindrical body is not touching the stem of the plant, but just the leaves, the linear goodness of fit is decreasing. The significance decreases together with the linear R2. The linear goodness’ of fit for the angle-dry plant mass relationship is the same as for the fresh plant mass. }
Parameters
Fresh Mass
Dry Mass
Angle
h
_{P}
h
_{A0}
m
_{P}
linear
linear
square
linear
linear
square
mean
min
max
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
(°)
(°)
0.300 0.100 0.450 0.93** 2.9 0.97** 0.94** 2.7 0.97** 35 9 47 0.400 0.100 0.497 0.88** 3.1 0.93** 0.90** 2.9 0.94** 29 8 40 0.400 0.200 0.450 0.91** 1.4 0.92** 0.88** 1.6 0.90** 11 3 19 0.500 0.100 0.519 0.94** 1.9 0.96** 0.95** 1.8 0.96** 26 9 36 0.500 0.100 1.000 0.94** 1.9 0.95** 0.94** 1.9 0.94** 18 3 29 0.500 0.100 1.500 0.96** 1.3 0.96** 0.96** 1.3 0.96** 14 1 23 0.500 0.100 2.000 0.97** 1.1 0.97** 0.96** 1.2 0.96** 11 0 19 0.500 0.200 0.497 0.94** 1.0 0.95** 0.91** 1.2 0.93** 9 2 16 0.500 0.300 0.450 0.62** 0.4 0.62 * 0.59** 0.4 0.59 * 3 2 4 0.600 0.100 0.533 0.95** 1.6 0.97** 0.95** 1.5 0.96** 23 9 31 0.700 0.100 0.556 0.95** 1.3 0.97** 0.95** 1.3 0.97** 21 8 28 0.800 0.100 0.576 0.93** 1.4 0.97** 0.94** 1.4 0.96** 20 7 26 0.800 0.200 0.556 0.93** 0.8 0.93** 0.90** 1.0 0.90** 7 1 12 0.800 0.300 0.533 0.24 † 0.8 0.33 † 0.21 † 0.8 0.35 † 3 1 5 0.900 0.100 0.604 0.93** 1.3 0.96** 0.94** 1.3 0.96** 19 7 25 1.000 0.100 0.618 0.88** 1.7 0.94** 0.89** 1.6 0.94** 18 6 23 ** Significance > 0.99 * Significance > 0.95 † not significant
Due to the similarity of linear and square goodness of fit, the standard error of estimate is calculated only for the linear goodness of fit. The standard error of estimate, calculated for the tested parameter settings, is rather low for irrigated rice at BBCH 39, with values ranging between 0.4 and 3 for fresh plant mass, and similarly for dry plant mass. There is a slight tendency towards a rise in the standard error of estimate with higher angles of deviation. Trends caused by the parameter settings are not visible.
Although the results of the exemplary growth-stages prove true for most parameters in the optimisation trials, differences can be seen over a wider range of growth-stages. This is especially true for the exemplary growth-stage in winter rye, where the standard error of estimate is considerably lower for the square R^{2} than for the linear. If the linear and square standard errors of estimate with their respective upper and lower limits are plotted against the measured growth-stages, then the influence of the crop growth becomes distinctive. The standard error of estimate of the square regression shows generally a better performance at the growth-stages BBCH 59 and 69, than in the linear regression (figure 22). For the earlier growth-stages only the standard error of estimate for the linear regression is calculated, due to the closeness of the linear and square R^{2}. It is obvious that something in the stem of winter rye is either changing or becoming more pronounced with heading and stem elongation.
The standard errors of estimate for the linear and square regressions in winter wheat and rice don’t show a difference between the different growth-stages, and the standard errors of estimate are only slightly increasing with later growth-stages. Thus the standard errors of estimate support the results of the plotted residuals.
Likewise show the upper and lower limits of the goodness of fit changes with the growth-stages. Of the three crops winter rye, winter wheat and irrigated rice, only winter rye shows a large difference between the linear and the square goodness’ of fit. This difference increases with increasing growth-stage of the crop (figure 23).
According to the R^{2}s (figure 23), the biomass sensor pendulum-meter shows a very good determination of winter rye crop biomass at the growth-stages BBCH 39 to 69. At the preceding growth-stage BBCH 32, the biomass sensor shows a decreased ability to determine crop biomass, and at the growth-stage 25, a measurement is not possible, because the lowest height of the cylindrical body is 0.1 m and the plant height is merely reaching that height. Winter wheat does not show this large difference between linear and square goodness of fit. According to the goodness of fit (figure 24), the pendulum-meter shows a very good determination of winter wheat crop biomass at the growth-stages BBCH 34 to 75, while at the preceding growth-stage BBCH 32, no winter wheat crop could be measured, and at BBCH 25 no measurement is possible. Due to the rapid growth at BBCH 32 and BBCH 34, it was not possible to test both for one crop, and the results for winter rye at BBCH 32 and winter wheat at BBCH 34 have to be seen jointly, thus indicating for both cereals that BBCH 32 is the first measurable growth-stage. But at BBCH 32 the ability to determine biomass is sub-optimal compared to the later growth-stages.
As figures 23 to 25 show, the earliest growth-stage, at which the biomass sensor pendulum-meter can be used, is BBCH 25 in irrigated rice, and BBCH 32 in winter wheat and winter rye. Due to the small plant size are there only a few potential pendulum parameters suitable for testing and measuring.
Winter Rye
The earliest growth-stage in winter rye that has a sufficient plant height to use the pendulum-meter is BBCH 32. The plant height is 37 cm to 54 cm. Table 13 shows the linear and square goodness’ of fit for dry and fresh plant mass, the standard error of estimate for the linear goodness of fit, and the average angle for the entire measurement strip.
Parameters
Fresh Mass
Dry Mass
Angle
h
_{P}
h
_{A0}
m
_{P}
linear
linear
square
linear
linear
square
mean
0.300 0.100 1.000 0.65** 3.7 0.66** 0.59** 4.0 0.64** 45 0.400 0.100 1.000 0.58** 3.9 0.63 * 0.52** 4.1 0.61 * 37 0.500 0.100 0.574 0.51** 3.2 0.55 * 0.46 * 3.4 0.54 * 39 0.500 0.100 1.000 0.49 * 3.5 0.54 * 0.43 * 3.8 0.52 * 34 0.500 0.100 1.500 0.52** 3.2 0.54 * 0.46 * 3.4 0.52 * 29 0.500 0.100 2.000 0.49 * 3.3 0.52 * 0.43 * 3.5 0.48 † 26 0.500 0.200 1.000 0.55** 3.8 0.55 * 0.50 * 4.0 0.51 * 15 0.600 0.100 1.000 0.45 * 3.2 0.50 * 0.38 * 3.3 0.47 † 32 0.700 0.100 1.000 0.53** 3.0 0.53 * 0.48 * 3.2 0.49 * 12 0.800 0.100 1.000 0.51** 2.9 0.51 * 0.45 * 3.0 0.46 † 13 0.900 0.100 1.000 0.50** 2.6 0.50 * 0.44 * 2.8 0.46 † 12 1.100 0.100 1.000 0.49 * 2.4 0.51 * 0.43 * 2.5 0.48 † 12 1.100 0.200 0.676 0.01 † 2.6 0.01 † 0.00 † 2.6 0.03 † 4 ** Significance > 0.99 * Significance > 0.95 † not significant
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
Like in the parameter optimisation trials for winter wheat BBCH 34 and rice BBCH 25, both, the linear and the square R^{2}s are very similar for all pendulum parameter settings. Nevertheless, the values for both goodness’ of fit are much lower for this growth-stage than for the later ones. For the most settings, the linear R^{2}s range between 0.4 and 0.65, the square R^{2}s perform a little bit better, both with a significance mostly between 0.95 and 0.99. In addition, there is a slight difference between the goodness’ of fit for fresh mass and dry mass. The standard error of estimate is with values less than 3.9 higher than for winter wheat and rice at their respective growth-stage. The biomass sensor is equally suitable for determining fresh and dry mass.
Tendencies are not clearly expressed for the height of the cylindrical body, because in this trial the tested heights of the cylindrical body touch only the lower half of the plant. The influence of the mass of pendulum on the R^{2}s is also not pronounced. The height of the pivot point tends to influence the measurements. In this trial it shows, the lower the height of the pivot point, the better is the R^{2}, which goes hand in hand with an increase in average angle of the test strip.
Winter Wheat
BBCH 34 is the earliest growth-stage in winter wheat, tested for a parameter optimisation trial (table 14), although measurements at BBCH 32 were possible.
Parameters
Fresh Mass
Dry Mass
Angle
h
_{P}
h
_{A0}
m
_{P}
linear
linear
square
linear
linear
square
mean
0.300 0.100 1.000 0.81** 3.0 0.83** 0.78** 3.2 0.79** 49 0.400 0.100 1.000 0.81** 2.6 0.82** 0.78** 2.8 0.79** 43 0.500 0.100 1.000 0.88** 1.6 0.88** 0.84** 1.9 0.84** 38 0.500 0.100 2.000 0.86** 1.9 0.90** 0.81** 2.3 0.84** 30 0.500 0.200 1.000 0.82** 2.4 0.83** 0.77** 2.6 0.78** 20 0.600 0.100 1.000 0.83** 1.9 0.86** 0.77** 2.2 0.79** 35 0.700 0.100 1.000 0.82** 1.7 0.86** 0.74** 2.0 0.78** 34 0.700 0.140 1.000 0.80** 1.9 0.86** 0.73** 2.3 0.78** 29 0.700 0.170 1.000 0.80** 2.2 0.86** 0.74** 2.5 0.79** 24 0.800 0.100 0.635 0.82** 1.3 0.84** 0.75** 1.6 0.77** 34 0.800 0.100 1.000 0.80** 1.5 0.84** 0.72** 1.8 0.75** 32 0.800 0.100 1.500 0.84** 1.5 0.87** 0.77** 1.8 0.80** 28 0.800 0.100 2.000 0.83** 1.6 0.86** 0.76** 2.0 0.78** 25 0.800 0.140 1.000 0.81** 1.7 0.86** 0.74** 2.0 0.78** 27 0.800 0.170 1.000 0.77** 2.1 0.80** 0.70** 2.4 0.72** 23 0.800 0.200 1.000 0.71** 2.2 0.71** 0.63** 2.5 0.63 * 20 0.800 0.240 1.000 0.51** 2.6 0.56 * 0.43 * 2.8 0.49 * 13 0.800 0.270 1.000 0.32 † 2.7 0.42 † 0.24 † 2.8 0.35 † 9 0.800 0.300 1.000 0.38 * 1.6 0.41 † 0.27 † 1.7 0.30 † 5 0.900 0.100 1.000 0.81** 1.4 0.84** 0.73** 1.6 0.75** 30 1.100 0.100 1.000 0.81** 1.2 0.84** 0.72** 1.5 0.74** 28 1.100 0.200 1.000 0.66** 1.9 0.67** 0.60** 2.1 0.61 * 17 ** Significance > 0.99 * Significance > 0.95 † not significant
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
By testing growth-stage BBCH 34 instead of BBCH 32 it was possible to elaborate more clearly the importance of plant height and stem elongation for the measurements with the pendulum-meter. The importance of these earliest measurable growth-stages is not only highlighted by the fact, that there are fewer for biomass sensing suitable pendulum parameters, but they are at the same time crucial growth-stages for the application of plant growth-regulators and fungicides. The plant height at this growth-stage was 44 cm to 50 cm.
Table 14 shows the linear and square goodness’ of fit for dry and fresh plant mass, the standard error of estimate for the linear goodness of fit, and the average angle for the entire measurement strip. Similar to the other parameter optimisation trials, both goodness’ of fit are very similar for all pendulum settings. Nevertheless, the values for both goodness’ of fit are considerably lower
Strong tendencies are visible for the height of the cylindrical body. While all settings with a height of the cylindrical body of 0.1 m perform well, an increase from 0.1 m to 0.3 m in the height of the cylindrical body has a strong effect on the goodness’ of fit, decreasing their values from 0.8 to 0.3. A considerable decrease show settings with higher heights of the cylindrical body than 0.2 m. More or less, a height of the cylindrical body touching the upper half of the plant doesn’t perform well, while the settings touching the lower half of the plant show a good performance. There is also a slight trend to an increased R^{2} with an increase in the mass of pendulum.
Rice
The earliest growth-stage in rice having sufficient plant heights to use the pendulum-meter is BBCH 25, respectively 28 DAT. The plant height at this growth-stage was 33 cm to 36 cm. Table 15 shows the linear and square R^{2}s for dry and fresh plant mass, the standard error of estimate for the linear goodness of fit, and the average angle for the entire measurement strip.
Similar to the other parameter optimisation trials, both goodness’ of fit are very high for a height of cylindrical body of 0.1 m, with values between 0.91 and 0.96. The standard error of estimate is at the same time rather low with values less than 2.2. The square goodness’ of fit perform slightly better than the linear goodness’ of fit, all with a significance above 0.99. The biomass sensor is equally suitable for determining fresh mass and dry mass.
Strong tendencies are visible for the height of the cylindrical body. While all settings with a height of the cylindrical body of 0.1 m perform well, an increase from 0.1 m to 0.2 m in the height of the cylindrical body has a strong effect on the R^{2}s, decreasing the values to 0.6 - 0.8. A setting with 0.3 m height of the cylindrical body decreases the R^{2} to less than 0.3, with an average angle for all measurements of 2°, which is almost no deviation. There is also a slight trend to decrease the goodness of fit with an increase in the mass of pendulum.
Parameters
Fresh Mass
Dry Mass
Angle
h
_{P}
h
_{A0}
m
_{P}
linear
linear
square
linear
linear
square
mean
0.300 0.100 0.450 0.90** 2.1 0.93** 0.89** 2.2 0.91** 25 0.400 0.100 0.497 0.91** 1.7 0.96** 0.91** 1.7 0.94** 23 0.400 0.200 0.450 0.80** 0.8 0.80** 0.77** 0.8 0.78** 6 0.500 0.100 0.519 0.95** 1.0 0.97** 0.94** 1.1 0.96** 19 0.500 0.100 1.000 0.96** 0.7 0.96** 0.95** 0.8 0.95** 13 0.500 0.100 1.500 0.94** 0.7 0.94** 0.92** 0.8 0.93** 9 0.500 0.200 0.497 0.63** 0.9 0.64* 0.59** 0.9 0.60 * 5 0.600 0.100 0.533 0.95** 0.9 0.97** 0.94** 1.0 0.95** 18 0.600 0.200 0.519 0.69** 0.7 0.75** 0.66** 0.7 0.73** 5 0.600 0.300 0.497 0.06 † 0.5 0.23 † 0.06 † 0.5 0.25 † 2 0.700 0.100 0.556 0.93** 0.9 0.97** 0.93** 0.9 0.95** 17 0.800 0.100 0.576 0.92** 0.9 0.96** 0.91** 0.9 0.95** 15 0.800 0.200 0.556 0.69** 0.6 0.70** 0.68** 0.6 0.69** 4 0.900 0.100 0.604 0.91** 0.9 0.96** 0.90** 1.0 0.95** 14 1.000 0.100 0.618 0.85** 1.2 0.94** 0.85** 1.2 0.93** 14 1.000 0.200 0.604 0.73** 0.6 0.75** 0.73** 0.6 0.75** 4 ** Significance > 0.99 * Significance > 0.95 † not significant
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
The accuracy of biomass determination, presented as fresh mass or dry mass, is in the exemplified growth-stages, as it is for the most growth-stages, sufficient to very good. The pendulum parameters mostly don’t show an influence on the quality of the regressions in all three crops and for most of the growth-stages. The pendulum parameter that is usually differing in terms of goodness of fit is the height of the cylindrical body h_{A0}, which is also most important for the measurement due to the measurement principle, since it decides where the plants are touched. Therefore, there are large differences between the goodness of fit when the plant stems are touched, and those few parameter settings when due to a high h_{A0} only the tops of the plants and the leaves were touched. As long as the plant stems are in contact with the cylindrical body, the goodness of fit is high, resulting in the advice, that the cylindrical body always has to be in contact with the stems for a proper measurement. These results support the correlation between bending rigidity and fresh weight found by HITAKA 1968. For the height of the pivot point and the mass of the pendulum, no optimisation can be considered via the goodness of fit. Similarly, PALAZZO & LEE 1986 couldn’t find an influence of the size of a disk-meter on R^{2}. The
In rice, all growth-stages show only slight differences between the linear and square regressions with low standard errors of the regression equation. For the disk-meters and plate-meters a low standard error of the regression was also reported (HARMONEY et al. 1997). The slight difference between linear and square regression is explained by the non-linearity of the force-angle relationship of the pendulum. The earliest tested growth-stage BBCH 25 also shows good results, as do the later growth-stages, for those parameters fully in contact with the plant stems. Earlier measurements are not possible due to the small plant heights. The standard error of most regressions can be considered low to medium, and it increases with plant growth.
The results for winter wheat are very similar to the ones obtained for rice, except that at the growth-stage 25 no measurement is possible due to the low plant height. A very similar device was used by (EL-SAYED YOUSSEF GHONIEM et al. 1980) to test for grain loss of the wheat ears before harvest. A physical effect on the ear or panicle was not observed despite the findings of EL-SAYED YOUSSEF GHONIEM et al. 1980. A potential effect of the measurements on the flowering cereals is not evident, but has to be observed.
The results in winter rye are differing in several parts. The measurements at BBCH 32 result in low accuracy of determination of biomass due to the low plant height. A sensing of biomass at BBCH 69 may be impossible due to the randomly distributed appearance of BRAZIER stem buckling (NIKLAS 1998). This stem buckling in winter rye can start as early as heading and is caused by the decrease of stem bending rigidity (SPIEWOK 1970, SPIEWOK 1974, SKUBISZ 1984, NIKLAS 1990, CROOK & ENNOS 1994), senescence of leaf sheaths (HITAKA 1968), and the increase of dry matter content of the stems (SPIEWOK 1970, SPIEWOK et al. 1970, SPIEWOK 1974). These causes for stem buckling at later growth-stages might as well be associated with the different performances of the linear and square regressions. In winter rye there is a large difference between the linear regression equations and the square regression equations in the accuracy of determining biomass throughout the most growth-stages. The square regression fits much better, and with a lower standard error of the regression, than the linear, supporting the results of the plotted residuals. This is further pronounced by the curvilinear angle-force relationship of the pendulum-meter. Additionally showed the tested winter rye strips also a square relationship for the height of the plants and the biomass itself. Square relationships
It can be considered that the measurements in winter rye and winter wheat at BBCH 25 are not possible with the pendulum-meter, that at BBCH 32 the accuracy is low, but suitable as long as there are no other sensors for that growth-stage and purpose, and that at BBCH 34 the accuracy of determining biomass is high till the stems lodge or loose their elasticity.
The goodness of fit of the pendulum-meter fits in with the best results of the disk-meters and plate-meters, which reach in pure grass stands an R^{2} of 0.7 or higher (POWELL 1974, CASTLE 1976, EARLE & MC GOWAN 1979, BAKER et al. 1981, MICHELL & LARGE 1983, SHARROW 1984, STOCKDALE 1984, PALAZZO & LEE 1986, SCRIVNER et al. 1986, KARL & NICHOLSON 1987, PETERSON & HUSSEY 1987, LACA et al. 1989, GONZALEZ et al. 1990, MOULD 1990, MOULD 1992, VIRKAJÄRVI & MATILAINEN 1995, REEVES et al. 1996, HARMONEY et al. 1997, MOSIMANN et al. 1999). For most of the reported relationships between the height of the resting disk or plate and the grass yield, a linear regression was sufficient, but several researchers used a square regression (POWELL 1974, EARLE & MC GOWAN 1979, BAKER et al. 1981, STOCKDALE 1984, GONZALEZ et al. 1990, VIRKAJÄRVI & MATILAINEN 1995), and MOULD 1990 even used a cubic regression.
After knowing the accuracy of the repeat and the accuracy of biomass determination, it follows to proper understand the influence of the independent parameters on the dependent parameter. In the case of the biomass sensor pendulum-meter the question arises about the relationship between the angle of deviation as the dependent parameter on the one side and the three pendulum parameters as the independent variables on the other side. At best it is possible to re-calculate the angle of deviation if one or more pendulum parameters are changed.
Multiple regression was used to detect, which pendulum setting has the strongest influence on the angle of deviation. The option of forward stepwise regression allows to evaluate the increment at each step of the goodness of fit R^{2} of the multiple regression caused by entering several independent variables. It also evaluates, if it is worthwhile to keep a variable as a predictor in the model or to exclude it. The three independent variables used in this multiple linear regression were the height of pivot point h_{P}, the height of the cylindrical body h_{A0}, and the
Winter Rye BBCH 39
Range of R
^{2}
increment
Step in
h
_{A0}
0.10 – 0.42 1/2/3/
m
_{P }
0.31 – 0.41 1/2/
h
_{P}
excluded – 0. 45 1/2/3/excluded/
Winter Wheat BBCH 39
Range of R
^{2}
increment
Step in
h
_{A0}
0.47 – 0.85 1
m
_{P}
0.01 – 0.04 3
h
_{P}
0.05 – 0.43 2
Rice BBCH 39
Range of R
^{2}
increment
Step in
h
_{A0}
0.36 – 0.66 1
m
_{P}
0.13 – 0.49 2
h
_{P}
excluded – 0. 13 3/excluded/
While the number of the step-in of the multiple stepwise forward regression never change in the 12 plots of rice and winter wheat, it changes from plot to plot in winter rye. With regard to rice, the height of the cylindrical body enters as first variable, and the mass of the pendulum as the second, while the height of pivot point is the third variable, if not excluded. In winter wheat, as in rice, the height of the cylindrical body enters as first variable. But unlike rice, the height of pivot point h_{P} enters as second, and the mass of the pendulum as third variable, with no exclusion. In winter rye, all dependent variables enter as first or second into the regression in one or the other plot. And only the h_{P} is excluded in some plots.
Regarding the increment of R^{2}, the height of the cylindrical body has the highest influence on the angle of deviation in rice and winter wheat. The mass of the pendulum shows the second highest increment of R^{2} in rice, while it is negligible in winter wheat. When adding the height of the pivot point into the model, the increment in R^{2} is low in rice, while it enters as the second
The regression equations are the final result of the stepwise forward multiple regression. The regression equations were calculated separately for each of the 12 plots of a optimisation trial. Table 17 shows the equations for the multiple regression between the three independent variables height of pivot point h_{P}, height of cylindrical body h_{A0}, and mass of pendulum m _{P}, and the measured angle of deviation, measured in winter rye at growth-stage BBCH 39 and calculated for all 12 plots. In the first plot, the regression equation for the change of the angle y can be formulated as equation 12:
y = 89.9 -106.4 h_{A0 }(m) -8.3 m
_{P }(kg) -18.1 h_{P }(m). [12]
The regression equation is highly significant with a goodness of fit of 0.90. That means that the measured angle of the deviated pendulum decreased 106° with an increase of 1 m of h_{P}, decreased 8° with 1 kg increase of m _{P}, and decreased 18° with an increase of 1 m of h_{A0}. This does not necessarily mean, that an increase of 1 m of h_{A0} is possible. In the case of winter rye at BBCH 39, an increase of 1 m height of cylindrical body would rise the sensor above the crop, and a measurement would be impossible. The change of the measured angle caused by a change in h_{A0} is much larger than the change caused by h_{P} or m _{P}.
Plot
1
2
3
4
5
6
Intercept
89.9 88.2 91.4 95.8 88.1 22.8
h
_{A0 }
(m)
-106.4 -104.2 -101.9 -88.2 -100.9 -52.7
m
_{P}
_{
(kg)
} -8.3 -8.6 -8.3 -8.5 -8.7 -3.1
h
_{P}
(m)
-18.1 -17.3 -20.7 -22.6 -17.9 excluded
R
^{2}
0.90** 0.88** 0.89** 0.89** 0.89** 0.73**
FM (kg m
^{-2}
)
1.412 1.304 1.596 1.792 1.320 0.232
Plot
7
8
9
10
11
12
Intercept
13.2 63.0 79.4 75.7 73.5 68.9
h
_{A0}
(m)
-36.7 -124.1 -120.3 -129.3 -126.7 -133.8
m
_{P}
(kg)
-1.8 -6.4 -8.0 -7.7 -7.7 -7.3
h
_{P}
(m)
excluded -7.5 -13.8 -11.3 -10.1 -8.1
R
^{2}
0.68** 0.85** 0.88** 0.86** 0.88** 0.87**
FM (kg m
^{-2}
)
0.192 0.740 1.092 0.920 0.816 0.732 ** Significance > 0.99 * Significance > 0.95 † not significant
By comparing the regression equations with the fresh mass of the plots, it becomes visible that several variables are influenced by the biomass itself. The intercept of the regression equation and the coefficient of the variable height of pivot point h_{P} indicate a strong influence of the fresh mass, while there is only a low similarity between the fresh mass and the coefficients of the variables mass of pendulum m _{P} and height of cylindrical body h_{A0}. The goodness of fit of the regression equations also change between the plots. They range between 0.85 and 0.90, except for the 6^{th} and 7^{th} plot, where they drop to 0.73 and 0.68 respectively. All R^{2}s were highly significant. The multiple regression for the 12 plots in winter wheat BBCH 39 and rice BBCH 39 presented similar results caused by the influence of the biomass and are given in the appendix.
Through keeping two pendulum settings constant, while only changing the third, it is possible to calculate the influence of one such setting on the measured angle of deviation of the pendulum-meter. Simple linear regression was used to calculate the change of the measured angle of a plot, thus being able to see the change of the measured angle through the 12 plots of a test strip. Assuming, that some of the pendulum’s settings will show the influence of the biomass, as it was seen in the multiple regression equations. Calculation of the simple linear regression, as for the multiple regression, is based on the measurements obtained by the parameter optimisation trials.
The Effect of the Height of Pivot Point
Table 18 shows the results of the simple linear regression, their goodness’ of fit, and their corresponding fresh mass FM as a measure for the biomass, for a change of the independent variable h_{P} in all 12 plots for the crop winter rye measured at the growth-stage BBCH 39. Constant was h_{A0} at 0.2 m, and m _{P} at 1 kg. The linear regression equations for the crops winter wheat and rice are given in the appendix. All regression equations in winter rye show highly significant R^{2}s, with values between 0.67 and 0.93, and an outlier for the plot with the lowest fresh mass. The intercepts of the regression equations for 12 plots range between 5 and 74, but most of them are in a smaller range between 36 and 74.
Winter Rye BBCH 39
Plot
1
2
3
4
5
6
Intercept
65.1 63.3 67.0 74.4 63.8 12.0
Slope
-24.9 -23.8 -27.0 -29.3 -24.5 -4.1
R
^{2}
0.93** 0.91** 0.91** 0.92** 0.90** 0.67**
FM (kg m
^{-2}
)
1.412 1.304 1.596 1.792 1.320 0.232
Plot
7
8
9
10
11
12
Intercept
4.6 35.7 53.0 48.0 45.4 39.5
Slope
-1.0 -12.8 -21.2 -18.9 -16.8 -14.2
R
^{2}
0.11** 0.76** 0.88** 0.84** 0.81** 0.82**
FM (kg m
^{-2}
)
0.192 0.740 1.092 0.920 0.816 0.732 ** Significance > 0.99 * Significance > 0.95 † not significant
Constant Pendulum Parameters: h
_{A0}
0.2 m,
m
_{P}
1.0 kg
As in the multiple regression equations, the intercepts of the linear regression equation are all positive. Opposite to the intercepts, all the slopes of the linear regression equations are negative. That means that an increase of the variable height of the pivot point will always decrease the angle of deviation. The slopes in winter rye range between -1 and -29.
Both the intercepts and the slopes are strongly related to the amount of fresh mass of the corresponding plots. But while the intercept is positively influenced by the biomass, the slope is negatively related to it. This result supports the results obtained for the multiple regression.
Regarding the necessity to re-calculate the measured angle when using different settings, the influence of the biomass itself and the large differences between each regression equation will cause difficulties. Since calibration curves exist for a change of the height of pivot point every 0.1 m, it was of interest to calculate the difference in the angle of deviation caused by a change of the height of the pivot point of 0.05 m. 0.05 m is the most likely setting that was not covered by the optimisation trials. Therefore, the slopes of the two most extreme equations, observed in the plots 4 and 7 in winter rye, were used to calculate the largest possible difference. In plot 4, a re-calculated angle would be changed by -1.47°, while in plot 7 the angle would change by -0.05°. Thus giving a difference between the two most extreme equations of 1.4° as an error term for using one or the other equation for re-calculating existing calibration curves for a change of the height of pivot point of 0.05 m.
Simple linear regression was used to calculate the change of the measured angle of a plot for a change of the variable height of the cylindrical body h_{A0}, while keeping the other two variables constant. Table 19 shows the results of the simple linear regression, their goodness’ of fit, and their corresponding fresh mass FM, for a change of the independent variable height of the cylindrical body h_{A0} in all 12 plots for the crop winter rye at the growth-stage BBCH 59. The height of the pivot point was kept constant at 1.1 m, as was the mass of the pendulum at 1 kg.
All regression equations show highly significant goodness’ of fit, with values in winter rye between 0.74 and 0.93. The intercepts of the regression equations for 12 plots in winter rye range between 35 and 84, but most of them are lying in a smaller range between 63 and 84. As in the multiple regression equations, the intercepts of the linear regression equations are all positive. Opposite to the intercepts, all the slopes of the linear regression equations are negative. That means that an increase of the variable height of the cylindrical body will always decrease the angle of deviation. The slopes in winter rye range between -27 and -46. The intercepts and the slopes are both similar to the amount of fresh mass of the corresponding plots, but not as much as in the regression equations for the change of the pivot point. While the intercept is positively related to the biomass, the slope is negatively related to it. This result supports the results of the multiple regression for winter rye at BBCH 39.
Winter Rye BBCH 59
Plot
1
2
3
4
5
6
Intercept
76.7 83.5 83.6 83.6 81.2 39.3
Slope
-36.8 -32.7 -29.0 -26.5 -45.5 -43.6
R
^{2}
0.92** 0.91** 0.91** 0.92** 0.90** 0.74**
FM (kg m
^{-2}
)
1.640 1.847 2.003 2.070 1.295 0.429
Plot
7
8
9
10
11
12
Intercept
34.9 63.0 76.8 77.9 72.7 65.4
Slope
-37.8 -39.9 -38.4 -41.0 -40.4 -39.7
R
^{2}
0.77** 0.91** 0.93** 0.92** 0.90** 0.94**
FM (kg m
^{-2}
)
0.436 1.051 1.244 1.271 1.154 1.089 ** Significance > 0.99 * Significance > 0.95 † not significant
Constant Pendulum Parameters: h
_{P}
1.1 m,
m
_{P}
1.0 kg
The Effect of the Mass of the Pendulum
Simple linear regression was used to calculate the change of the measured angle of a plot for a change of the variable mass of the pendulum m _{P}, while keeping the other two variables constant. Table 20 shows the results of the simple linear regression, their goodness’ of fit and their corresponding fresh mass FM, for a change of the independent variable m _{P} in all 12 plots.
Winter rye was calculated for the growth-stage BBCH 39. The height of the pivot point is kept constant at 1.1 m, while h_{A0} is a constant at 0.2 m. All regression equations show highly significant goodness’ of fit, with values in winter rye between 0.86 and 0.98.
Winter Rye BBCH 39
Plot
1
2
3
4
5
6
Intercept
41.3 41.0 40.9 45.7 41.2 11.1
Slope
-4.8 -5.0 -4.9 -5.0 -5.4 -3.5
R
^{2}
0.98** 0.98** 0.98** 0.98** 0.98** 0.92**
FM (kg m
^{-2}
)
1.412 1.304 1.596 1.792 1.320 0.232
Plot
7
8
9
10
11
12
Intercept
6.1 25.1 33.8 31.3 31.5 28.3
Slope
-1.9 -4.4 -5.1 -5.1 -5.3 -5.2
R
^{2}
0.86** 0.95** 0.98** 0.98** 0.98** 0.98**
FM (kg m
^{-2}
)
0.192 0.740 1.092 0.920 0.816 0.732 ** Significance > 0.99 * Significance > 0.95 † not significant
Constant Pendulum Parameters: h
_{P}
1.1m, h
_{A0}
0.2m
Regarding the necessity to re-calculate the measured angle when using different settings, the influence of the biomass itself and the large differences between each regression equation will cause difficulties. But due to the smaller range of the slopes and the low values of the slopes, the differences between the lowest and the highest slopes is not as big a problem as with h_{A0} and h_{P}. Since measurements exist in the optimisation trials for a change of m _{P} in increments of 0.5 kg, it was of interest to calculate the difference in the angle of deviation caused by a change of the mass of the pendulum of 0.1 kg, a change that can be encountered by constructing a pendulum of different materials. Therefore, the slopes of the two most extreme regression equations, here observed in the plots 5 and 7, are used to calculate the largest possible difference. In plot 5, a re-calculated angle will change by -0.54°, while in plot 7 by -0.19°. Thus giving a difference between the two most extreme equations of 0.4° as an error term for using one or the other equation for re-calculating existing measurements for a change of m _{P} by 0.1 kg.
The Effect of the Length of the Pendulum
The influence of the length of the pendulum on the change of the angle of deviation was not determined since the length of the pendulum is the distance between the height of the pivot point and the height of the cylindrical body. In a contact measurement as it is with the pendulum-meter, the height of the cylindrical body is of importance. If the height of the cylindrical body is a constant, the length of the pendulum can be expressed as the height of the pivot point, as it is done in this context.
The strongest influence on the angle of deviation has the height of the cylindrical body h_{A0}, followed mostly by the mass of the pendulum, and the height of the pivot point is often the parameter with the lowest influence if not excluded. The coefficient of h_{A0} is the highest, followed by h_{P}, and the lowest coefficient is m
_{P}. The fresh mass is influencing the multiple regression between the three pendulum parameters versus the angle of deviation to various degrees. Due to this large influence of the biomass on the coefficients and the intercepts, a re-calculation of the angle of deviation into an untested parameter setting leads to a large error, and
The linear regressions between the single parameters h_{A0}, h_{P} and m _{P} versus the angle of deviation show changes of the equation with a change in biomass for the slopes of the regression or the intercepts or both. Therefore, a re-calculation of an angle of deviation by the regression equation results always in a bias due to the biomass, and a re-calibration seems advisable.
The slopes of the parameter h_{P} are negative in the linear regression with the angle of deviation because the angle in the potentiometer becomes more acute-angled, provided h_{A0} is the same during the measurement. The slopes of the parameter h_{A0} are negative due to the contact with higher parts of the stems. Several researchers (SPIEWOK 1970, GOWIN 1980, SKUBISZ 1984, NIKLAS 1990, CROOK & ENNOS 1994) reported an increase in bending rigidity E I from the ear to the root. The slopes of the parameter m _{P} are negative due to a deeper bending of the stems, thus narrowing the angle at the potentiometer. HITAKA 1968 used higher masses at ear base to deviate the stem more deeply.
A change of these relationships during crop growth is most likely, since several researchers found a change of the modulus of elasticity E during crop growth (GOWIN 1980, GAWDA & HAMAN 1983, SKUBISZ 1984, O’DOGHERTY et al. 1989, USREY et al. 1992, MOLDENHAUER & MOLDENHAUER 1994, O’DOGHERTY et al. 1995), although the second moment of the area I is not changing during crop growth (O’DOGHERTY et al. 1995).
Since determining the actual plant mass in the field is a time-consuming and destructive method without using the pendulum-meter as a sensor, most research was done relating crop density in terms of tiller density and stem density to yield, diseases, and crop models. Therefore, it is of high interest to know the relationship between the measurements of the pendulum-meter and other, here called secondary, plant parameters such as plant height, stem or tiller density, and with regard to rice the number of plant hills.
The relationship between the above mentioned plant parameters and the measured angle of deviation of the pendulum-meter was calculated using simple linear regression. Measurements were done with 2.5 m s^{-1} velocity and 75 Hz. The results for winter rye are shown in table 21, where the first row shows the BBCH growth-stage, and the next rows the used pendulum
The results show a highly significant goodness of fit of 0.59 between the measured angle and the number of tillers for the growth-stage BBCH 59, decreasing to a significant goodness of fit of 0.55 at the next growth-stage. The standard error of estimate of the regression equation is high with values between 13 and 15. No values were obtained for the earlier growth-stages due to a lack of time.
The regression results for the plant parameter average plant height are similar, with highly significant goodness of fit of 0.90 to 0.95 for the average plant height. The exception is the first measured growth-stage BBCH 32, where the average plant height has a slightly lower goodness of fit with 0.81. The standard error of estimate is in general medium to high. The average plant height shows a goodness of fit with the model in terms of goodness of fit and standard error. The goodness of fit for the average plant height is increasing with the growth-stages. The slopes for the average plant height are all positive, while the intercepts are all negative. The slopes for the number of tillers or stems are all zero, with mostly negative intercepts. Except for the last growth-stage, where it is slightly positive.
As a matter of fact, the plant height is the parameter that differentiated much more than the number of tillers or stems.
Growth-Stage
BBCH 32
BBCH 39
BBCH 59
BBCH 69
h
_{P}
(m)
0.3 0.4 0.8 1.1
h
_{A0}
(m)
0.1 0.2 0.6 0.8
m
_{P}
(kg)
1 3 0.5 1
Number of tillers or stems
Intercept
– – -27.7 -33.6
Slope
– – 0.3 0.2
R²
– – 0.59** 0.55 *
SE
– – 15.00 13.85
Average plant height
Intercept
-10.3 -39.5 -34.8 -24.0
Slope
142.1 129.7 80.2 45.4
R²
0.81** 0.90** 0.94** 0.95**
SE
2.76 4.45 5.74 4.49 ** Significance > 0.99 * Significance > 0.95 † not significant – no measurements
The results of the linear regression between the two plant parameters and the measured angle of deviation for winter wheat are shown in table 22, where the first row shows the BBCH growth-stage, and the next row the used pendulum settings. The first column gives the related plant parameter, and the second column gives the regression parameters. All plant heights are calculated in meters. Measurements were done with 2.5 m s^{-1} velocity, and 75 Hz frequency. The results show highly significant goodness of fit of 0.92 between the measured angle and the number of tillers at the growth-stage BBCH 39, with a very low standard error of estimate. The growth-stage BBCH 39 is the only one where the tiller number was counted.
The regression results for the average plant height are similar, with highly significant goodness of fit of 0.72 to 0.85 for the average plant height versus angle of deviation. The exception was the first measured growth-stage BBCH 34, where the average plant height shows no relation to the measured angle. The standard error of estimate is low to medium for the average plant height.. The goodness of fit for the average plant height is indifferent for the growth-stages. The slope for the number of tillers or stems is zero.
As a matter of fact in the winter wheat field, used here for this regression calculations, the plant height and the tiller or stem density are differentiating similarly in the same plots.
Growth-Stage
BBCH 34
BBCH 39
BBCH 59
BBCH 69
h
_{P}
(m)
0.5 0.5 0.8 0.7
h
_{A0}
(m)
0.1 0.3 0.4 0.3
m
_{P}
(kg)
1 1 1 1
Number of tillers or stems
Intercept
– -2.6 – –
Slope
– 0.2 – –
R
^{2}
– 0.92** – –
SE
– 3.85 – –
Average plant height
Intercept
12.6 -155.1 -60.3 -37.9
Slope
53.7 343.4 155.3 111.4
R
^{2}
0.05 † 0.84** 0.85** 0.72**
SE
4.57 5.34 5.97 5.37 ** Significance > 0.99 * Significance > 0.95 † not significant – no measurements
The results of the linear regression between the plant parameters, average plant height and tiller density, and the measured angle of deviation for rice are shown in table 23. The procedure was the same as in winter rye and winter wheat. The results show a highly significant goodness of fit of 0.91 between the measured angle and the number of hills for the growth-stage BBCH 39, decreasing to a significant goodness of fit of 0.33 at the next growth-stage, and reaches a highly significant goodness of fit of 0.72 for the growth-stage BBCH 65.
There is also a highly significant goodness of fit of 0.93 between the measured angle and the number of tillers for the growth-stage BBCH 39, and an R^{2} of 0.85 at the growth-stage BBCH 65. The number of hills and the number of tillers show low to medium standard errors. The regression results for the average plant height show lower values, with significant low R^{2} between 0.40 and 0.34. The standard error of estimate is in general medium to high. The slopes for the number of tillers and for the number of hills regression equations are all zero, with mostly negative intercepts.
As a matter of fact, and opposite to winter rye, the plant height is the parameter that differentiated the least, much less than the number of tillers or stems.
Growth-Stage
BBCH 39
BBCH 49
BBCH 65
h
_{P}
(m)
0.5 0.5 0.4
h
_{A0}
(m)
0.1 0.1 0.2
m
_{P}
(kg)
1 2 0.5
Number of hills
Intercept
-1.3 12.4 -20.8
Slope
0.8 0.5 2.3
R
^{2}
0.91** 0.33 * 0.72**
SE
2.30 5.39 6.76
Number of tillers or stems
Intercept
-1.6 – -3.8
Slope
0.1 – 0.1
R
^{2}
0.93** – 0.85**
SE
2.01 – 4.89
Average plant height
Intercept
-68.9 -41.4 -96.1
Slope
209.5 139.1 194.2
R
^{2}
0.40 * 0.34 * 0.36 *
SE
5.84 5.38 10.21 ** Significance > 0.99 * Significance > 0.95 † not significant – no measurements
From the view of a biomass sensor is the possibility of sensing additional plant parameters, such as plant height and tiller density, most interesting, since biomass is not a common basis for decision making in agriculture. Instead of biomass, other plant parameters, such as tiller density and plant height, are widely used. The ability of the pendulum-meter to sense these plant parameters depends mainly on the factor that is more heterogen. That means if the plant height is the least heterogen parameter as in rice, the biomass sensor determines with a high goodness of fit the number of tillers, but the angle of the pendulum-meter has no or just a low relationship with the plant height. If the plant height is much more heterogen than the tiller density as in winter rye, the biomass sensor has a high goodness of fit with the height of the plants, but the angle of the pendulum-meter has no or only a low relationship with the tiller density. If both factors, the tiller density and the plant height are equally heterogen, the biomass sensor can determine both factors with similar and high goodness’ of fit, mostly with an R^{2} higher than 0.7.
In standard agricultural literature concerning rice, the number of hills is well correlated with biomass, but in these tests it shows a good performance only once, but in the other test there is a low R^{2}, which might be caused by uneven damage by rats.
When using a sensor with a dynamic measurement principle, it can be assumed that the velocity of the carrier has a major influence on the measurements. To clarify the influence of the velocity on the angle of deviation, tests were conducted with an identical parameter setting but different velocities. The velocities ranged between 0.5 m s^{-1} and 3.5 m s^{-1}, arranged in 0.5 m s^{-1} steps, but the five repeats were not exactly in the wanted 0.5 m s^{-1} order, due to the hand-pushed acceleration. The tests were done for all crops at different growth-stages, usually on a 5 m^{2} plot. Due to the high similarity of the results are the majority of the tests given in the appendix, and an exemplary result is presented here. As shown in figure 26 is the speed of elementary influence on the measured angle of deviation, tested with the pendulum setting 0.8 m of h_{P}, 0.3 m of h_{A0}, and 1 kg of m _{P}, and a frequency of 75 Hz.. Also given in figure 26 are the linear and square regressions, their formula and goodness of fit for the relationship between carrier speed and angle of deviation in one plot in winter rye at BBCH 39.
The carrier speed changes the measured angle of deviation from 31° at a velocity of 0.5 m s^{-1} to 44° at a speed of 3.5 m s^{-1}. The relationship is slightly better described by the square regression equation than by the linear equation, though the difference in the goodness of fit is small, 0.99
Since in the aforementioned optimisation trials the biomass was showing a considerable influence on the angle of deviation, one velocity test was done in winter wheat 1999 over 60 meters, or 12 plots respectively. Of the twelve plots, only the plots 4 - 12 were used, since the high speeds of 3.5 m s^{-1} were not reached in the first three plots.
The influence of the carrier speed on the angle of deviation in plots with different biomass, or initial angle of deviation at a speed of 0.5 m s^{-1} respectively, are shown in figure 27 for a visual impression, and in table 24 for the linear regression equations and goodness’ of fit. The crop was measured in growth-stage BBCH 49, with the pendulum setting 1.0 m h_{P}, 0.3 m h_{A0}, 1 kg m _{P}, and 75 Hz measurement frequency.
As shown in table 24, the goodness of fit R^{2} is for the most plots higher than 0.9, except for the plots 6 and 7, where the goodness of fit is 0.8, and plot 5, where the goodness of fit is only 0.03. As an explanation has to be considered, that the highest biomass, or in that case, the highest initial angle of deviation, is in plot 4. In plot 5, and to a much lesser degree also in the plots 6 and 7, the angles are biased at high carrier speeds by the high deviation in plot 4. Nevertheless, in all plots except plot 5 is a close relationship between the speed and the angle of deviation.
Visible in figure 27 is the influence of the initial angle, hence the biomass: the higher the initial angle at a speed of 0.5 m s^{-1}, the higher the slopes becomes, as well as the intercepts, for the regression between the carrier speed and the angle of deviation. Plot 5 shows here no change of the angle of deviation with the change in velocity (black dotted line in figure 27).
Linear Regression Equation
Goodness of Fit
Angle of Deviation (°)
Plot
Slope
Intercept
R
^{2}
at 0.5 m s
^{-1}
at 3.3–3.8 m s
^{-1}
4
5.11 15.7 0.99** 19.09 34.38
5
-0.19 4.9 0.03 † 3.83 2.45
6
2.94 0.8 0.81** 2.38 12.70
7
2.49 1.2 0.88** 3.36 12.09
8
2.60 2.1 0.92** 4.62 11.61
9
3.02 6.1 0.98** 7.92 16.74
10
3.69 14.1 0.99** 16.42 27.33
11
4.67 14.1 0.99** 17.81 31.09
12
2.47 7.9 0.97** 9.96 16.55 ** Significance > 0.99 * Significance > 0.95 † not significant
Pendulum setting: 1.0 m h_{P}, 0.3 m h_{A0}, 1 kg m
_{P}, 75 Hz frequency.
To correct the angle of deviation in case of 1 m s^{-1} velocity changes of the tractor in the field would result in a difference of more than 2.5°, depending on which slope would be used to re-calculate the angle. Considering the highest possible change of the angle of deviation of 5° in plot 4 when the speed was changed by 1 m s^{-1}, that difference is high. Due to the fact, that the angle versus speed relationship is always close for different cultures and growth-stages, and the biasing effect of the biomass itself, the other speed results are given in the appendix.
Discussion
The angle of deviation is in the range of less than 4 m s^{-1} closely related to the speed. The goodness of fit is always very high, except when plots with a low biomass follow right behind a plot with a high biomass. The square goodness of fit is almost perfect in the tested speed range, but the linear regression is almost as good as the square regression. The difference is very similar to the force-angle relation of the pendulum-meter in figure 9, but according to equation 2, the mass moment of inertia is dependent on the square power of the carrier speed. Regarding the small differences between the square and linear regressions, a linear regression is sufficient to describe the angle versus speed relationship. According to equation 2, the mass moment of inertia is changed by a factor 6.25 when the speed increases from 1 m s^{-1} to 2.5 m s^{-1}. The bending moment, according to equation 3, is not depending on the carrier speed, hence it is considered a constant in the speed measurement. The friction is also not changing with speed as several works stated (RICHTER 1954, SHINNERS et al. 1991, USREY et al. 1992). Hence, the mass moment of inertia, together with the higher number of stem tested per unit time, is the cause of the changes in the angle of deviation with an increased speed.
The speed-angle regressions clearly show an influence of the biomass, meaning a high angle at low speeds will be increased much more by an increasing speed than a lower angle. A re-calculation during field measurements by using the slopes of the regression results in a large error. Therefore, it is advisable to keep the speed constant within the field, or within the measurements that are compared.
The speed might have biased the goodness of fit in the parameter optimisation trials, because while pushing the carrier, little changes in speed of 0.5 m s^{-1} were always possible and included in the goodness of fit of the parameter optimisation trials.
The force which is applied by the wind upon the plants results in movements and vibrations of the stems, forcing the stems to bent. Mechanically the force of the wind is similar to the force the biomass sensor is putting on the plants.
The effect of the headwind in the range of 0 to 4 m s^{-1} wind speed in rice at BBCH 65 is illustrated in figure 28. The plant height of the 5 m^{2} plot was on average 65 to 70 cm, the pendulum-meter was adjusted to 0.6 m h_{P}, 0.3 m h_{A0}, 0.497 kg m _{P}, and the carrier speed was 1 m s^{-1}. The blue dots are the plot averages and the differences between the five replicates are almost as high as the effect of the constant headwind. The linear regression is not significant with an R^{2} of 0.13, and the square regression is significant with an R^{2} of 0.27. The intercepts of the regression equations are, in these cases, the average measurements without wind. Nevertheless, there is to some degree a tendency to increase the angle of deviation with an increase of headwind higher than 2 m s^{-1}.
The effect of the water height of standing water in irrigated rice at BBCH 39 on the measurements of the pendulum-meter is shown in figure 29. The rice crop was measured with various water heights, ranging of 0.5 cm to 11.5 cm. The plant height in the 5 m^{2} plot was on
Weeds are a major problem in plant production and their occurrence in patches with high population densities of one or several weed species give reason to determine the possible effect of two common weed species, creeping thistles and loose silky-bent, on the measurements.
The Effect of Creeping Thistles
With their strong woody stems, creeping thistles (Cirsium arvense (L.) Scop.) are different from the slender cereal stems. The influence of this weed species on the measurements was tested in a 5 m^{2} winter wheat plot at growth-stage BBCH 39. In this plot was a single thistle 10 cm higher than the surrounding crop. Figure 30 shows the on-line measurements of the plot with the thistle and the same on-line plot measurements when the thistle was cut out. The pendulum-meter was adjusted to 0.8 m h_{P}, 0.1 m h_{A0}, 0.635 kg m
_{P}, 75 Hz measurement frequency, and 2.5 m s^{-1} carrier speed. The calculated plot average of the angle of deviation is 16.2° with the thistle, and 12.6°
The Effect of Loose Silky-Bent
Loose silky-bent (Apera spica-venti (L.) Pal. Beauv.) often appears in numbers after heading of the cereal crops. The effect of this weed species was tested in a 5 m^{2} plot of winter wheat at growth-stage BBCH 69. In the plot were 171 silky-bents with an on average 20 cm higher plant height than the surrounding crop. The silky-bents were distributed all over the plot. The pendulum-meter was set to 1.1 m h_{P}, 0.3 m h_{A0}, 0.657 kg m _{P}, 75 Hz frequency, and 2.5 m s^{-1} carrier speed. The calculated plot average is 55.1° including all of the silky-bents, and 53.7° without the silky-bents, resulting in a difference of 1.4°, or less than 5 % of the plot average.
The Effect of Day and Daytime
Since the use of the pendulum-meter for sensing on-line crop biomass for site-specific applications is not limited to a few hours, the time of the measurements during the day and on
On the 3^{rd} of May 1998, plot 1 has an average of 36.3° of all measurements during that day, while in plot 2 the average is only 4.5°. The standard deviation of all measurements of that day is 0.35° in plot 1, and 0.34° in plot 2. The measurements are stabile for the two plots during the day. The average of the measurements of the 4^{th} of May is 39.6° for plot 1, and 4.3° for plot 2. The difference of the average measurements from the 3^{rd} of May to the 4^{th} of May is +3.3° for plot 1, and -0.2° for plot 2. Plot 1 has a high biomass as indicated by the large angle of deviation and has a large increment during night time, while plot 2 has a low average angle, or a low biomass respectively, and no change during night time.
The Effect of Growth-Stage
Crop growth is determined by a large increase in crop biomass, and between growth-stages BBCH 30 to 69 in a large increase in plant height. Because of the high accuracy of determining biomass of the pendulum-meter, a biasing effect of rapid growth might be possible. Therefore,
Remarkable is the closeness of the lower regression slopes of the growth-stages BBCH 39 and 59, while the upper slopes differ almost 30°. This result is supporting the growth pattern evident in figure 31 for the measurements during two consecutive days, where the plot with a low biomass didn’t show a change in the measurements, whereas the plot with a high biomass showed crop growth within two days. Since in the year 1998 there were only 11 days between those growth-stages, the difference of 30° between the two regression lines at BBCH 39 and 59 in 11 days resulted in a crop growth of almost 3° per day on average. Hence, rapid crop growth can bias measurements taken over several days in one field, or taken on one day in a field with differences in crop development. The pooled square regression between the growth-stage BBCH 39 and 59 has following equation 13:
y = -8.58 x^{2} +48.19 x -7.23 [13]
The pooled square regression results in an R^{2} of 0.64 with a standard error of the regression equation of 12.40, and runs between the two regression lines given in figure 32.
With regard to irrigated rice, two different varieties from two different seasons and two different seeding methods were pooled together by a square regression. Figure 33 shows the resulting square regression of the angle versus dry mass with confidential limits for IR 64 and IR 72. While IR 64 is a machine-transplanted irrigated rice grown in the dry season, IR 72 is a direct-seeded irrigated rice grown in the wet season. Both varieties were tested with a setting of 0.5 m h_{P}, 0.2 m of h_{A0}, m _{P} of 1 kg, 75 Hz frequency, and 2.5 ms^{-1} carrier speed. Though they were tested at different dates, they were both in the growth-stage BBCH 65. The pooled square regression for them has an R^{2} of 0.94, and a standard error of the regression of 3.53. The estimated 95 % confidential limits are narrow, but more than 5 % of the dots are out of the limits.
The yearly influence on the biomass versus angle relationship in winter rye was detected at the growth-stage BBCH 39. The pendulum was set to 1.1 m h_{P}, 0.3 m of h_{A0}, m _{P} of 0.657 kg in the years 1998 and 1999. A pooled square regression is obtained with an R² of 0.88, and a standard error of the regression of 7.59, for the dry mass versus angle relationship with the following regression equation 14:
y = -8.8678x^{2} + 53.529x -5.873. [14]
A pooled square regression of two different winter wheat varieties, ZENTOS and BATIS, in two
y = -1.5496x^{2} + 13.336x +34.475. [15]
The Influence of Biomass-Height-Ratio
Regarding the influence of bending moment and bending distance on the angle of deviation, it is so far not clear, whether the crop height or the crop density, hence the biomass-height ratio can bias the measurements. Figure 34 shows the result of: first, the 12 plots with large changes in crop height, and second, the change in biomass versus angle relationship without the influence of the crop height. Therefore, a winter wheat crop at BBCH 69 was tested with the parameter setting of 0.8 m h_{P}, 0.3 m of h_{A0}, m _{P} of 0.595 kg, 75 Hz measurement frequency, and 2.5 m s^{-1}. A test strip of 12 plots with natural differences in crop height was measured, and then one plot with a high crop height was systematically thinned by cutting randomly 25 stems out of the plot between the measurements.
The two regression lines start at the same point, but spread wider the lower the amount of biomass gets in the plot. The lower parts of the regression lines have differences of more than 10°, with the systematically thinned plot being higher than the natural plot with a low plant height. That indicates that the biomass-height-ratio can bias the measurements, since the crop height has a larger influence than the crop density regarding the same amount of biomass.
Stem inclination is usually the first step of lodging in cereals. Due to the measurement principle, a permanent lean of the stem from the vertical position may effect the measurements. Therefore, one winter wheat plot and one winter rye plot were tested in both directions of the stem inclination at different growth-stages to clarify the biasing effect of inclination. Table 25 shows the results of the measurements for the one way with a slight inclination of less than 5°, and the return way against the same slight inclination. In the case of winter wheat, there is only a minor difference of 0.5° between the two measurement directions at BBCH 32 and 39, which increases to almost 1° at later growth-stages, where the slight inclinations of the stems become visible as a 5° difference. The difference between the measurement directions is much higher in winter rye, with 0.2° at BBCH 39, and increasing to 1.5° – 4-5° at later growth-stages, when the stem is slightly inclining and the ear is turning downwards.
BBCH Growth-Stage
32-34
39
59
69
Winter rye; 0.8 m h
_{P}
, 0.3 m h
_{A0}
, 1.000 kg
m
_{P}
, 75Hz, 1 m s
^{-1}
one way (°)
– 26.69 45.09 64.27
return way (°)
– 26.55 43.68 59.79
difference (°)
– 0.14 1.41 4.48
Winter wheat; 1.1 m h
_{P}
, 0.1 m h
_{A0}
, 0.696 kg
m
_{P}
, 75 Hz, 1 m s
^{-1}
one way (°)
26.69 41.52 61.10 –
return way (°)
27.17 41.97 61.98 –
difference (°)
0.47 0.45 0.88 –
For an inclination of 30° of winter wheat stems at BBCH 69, the results are shown in table 26. That test was done with two different masses of the pendulum, 2 kg and 0.657 kg. The results show a difference of less than 1° for both masses, with a higher difference for 2 kg mass of the pendulum.
Winter wheat; 30° Inclination of the stem, 1.1 m h
_{P}
, 0.3 m h
_{A0}
, 75 Hz, 1 m s
^{-1}
, BBCH 69
m
_{P}
(kg)
2.000 0.657
Measurement in one way (°)
27.73 45.24
Measurement in return way (°)
28.72 46.01
Difference (°)
0.99 0.77
The effect of the depth of the tramline on the angle of deviation was determined indirectly by changing the two pendulum parameters h_{P} and h_{A0} simultaneously in 12 plots with 75 Hz and 2.5 m s^{-1}. Though that was not usually a part of the optimisation trials, one of the optimisation trials in rice gave sufficient data to calculate this effect. Based on an original measurement with the pendulum parameter setting of 0.6 m h_{P}, 0.3 m h_{A0}, and 1 kg m _{P}, the measured angles of deviation ranged between 16.6° and 55.4°. A tramline depth of 10 cm would result in the parameter setting of 0.5 m h_{P}, 0.2 m h_{A0}, and 1 kg m _{P}, which had a range in angles of deviation from 34.6° to 69.4°. The bias due to the depth of the tramline of 10 cm is here the difference between the two settings, hence the bias would range from 14.0° to 18.4°, approximately 30 % to 100 % of the original value.
Headwind in the range of 0 m s^{-1} to 4 m s^{-1} has a low influence on the angle of deviation of the pendulum-meter. The reduction of the angles from 0 m s^{-1} to 2 m s^{-1} might be caused by slight non-visible changes in the position of the stems, while reclining back into the upright position, which was reported by HITAKA 1968 who used this difference to determine lodging susceptibility. The increase in angles between the wind speeds 2 m s^{-1} and 4 m s^{-1}, and the much better fit of the square regression, supports the results of HITAKA 1968 who found a similar relation between the wind force on one tiller and the wind speed. Nevertheless, the reported bending angle of 10° at a wind speed of 3 m s^{-1} is much larger than the change in the angle of deviation of the pendulum-meter. The pendulum-meter measurements are much less influenced by the wind than the stems themselves, probably because the cylindrical body touches the stems at much lower heights than the wind is penetrating into the crop. But even higher wind speeds will have a larger influence on the angle of the pendulum-meter regarding the results of HITAKA 1968.
The effect of water height on the pendulum-meter is low and the influence is only visible at 8 cm and 12 cm water height, which is not the usual water height in irrigated rice. Recommended are 3 cm. Although water height might change the angle through different growth pattern of the elongated basal internodes (WU 1965), it is itself negligible as a biasing factor.
The effects of weeds can range from not pronounced to a strong influence on the angle of deviation, depending on the strength of the stem of the weed species, their growth-stage, plant height, and their number. Loose silky-bent has a very thin stem, and is biasing the pendulum-meter only slightly when in large numbers. Creeping thistles can bias the biomass measurements
The measurements are stabile during one day, but rapid growth can change the angle of deviation from one day to the other, thus also biasing the goodness of fit calculated in the parameter optimisation trials, which took usually 2 days for the measurements. The 0.6 mm of rain in the second day didn’t influence the measurements, although HITAKA 1968 reported an increase in breaking strength due to wetness. Daily growth will also bias the measurements within large fields where there are sites with plants in different stages of development, like BBCH 32 and BBCH 34. The growth-stages in European cereals with rapid growth are BBCH 32 to BBCH 59 depending on the weather conditions. Rapid plant growth needs high temperatures, in cool temperatures there is much more time between the growth-stages, hence the bias is much smaller within a field. Irrigated rice shows a very regular growth and there the bias is much smaller.
The growth-stages usually need different regression equations, although a pooling in several cases is possible but results in a large standard error of the regression and a decline of R^{2}. Consequently are the parameter settings well suited in one growth-stage different from the favourable settings in another growth-stage, thus calling for different favourable settings for most growth-stages. It follows, that using a single parameter setting for all growth-stages and crops doesn’t seem advisable, because for choosing a favourable pendulum setting this setting has to be related to plant size which is varying with seasons and years. The differences in the regression equations for different growth-stages was often stated for the disk-meters and plate-meters (POWELL 1974, CASTLE 1976, EARLE & MC GOWAN 1979, BAKER et al. 1981, MICHELL & LARGE 1983, STOCKDALE 1984, PALAZZO & LEE 1986, SCRIVNER et al. 1986, KARL & NICHOLSON 1987, PETERSON & HUSSEY 1987, BRYAN et al. 1989, GONZALEZ et al. 1990, MOULD 1992, VIRKAJÄRVI & MATILAINEN 1995, HARMONEY et al. 1997). Changes in bending rigidity (LACA et al. 1989) and dry matter content (POWELL 1974, STOCKDALE 1984) were suggested as reasons for the differences in the regressions at different growth-stages, but (BAKER et al. 1981) found no change due to maturity. The dry matter content of a plant is easier to determine than bending parameters, but the dry matter content differs for the growth-stages (SPIEWOK 1974) and influences strongly the bending
The pooled regression for two rice varieties, with two planting methods, and grown in two different seasons, shows a high R^{2} and a medium standard error of the regression. This is interesting from several viewpoints. The two planting methods usually result in a different lodging susceptibility (NISHIYAMA 1986, CROOK & ENNOS 1994), but the sensor is not sensitive to these differences, such as the reported differences in bending angle due to planting depth (HITAKA 1968). The two different seasons result in different growth habits (WU 1965), such as in the wet season there are heavier main tillers, poorer leaf sheath protection, lower breaking strength, longer basal internodes, and longer stems. The angle versus biomass relation is not sensitive to these factors in rice. The two varieties are not only almost identical in phenotype, they also have the same biomass-angle relationship for that growth-stage. The pooled regression of the two wheat varieties is not performing as well as the two rice varieties, but still with a sufficient R^{2 }and an medium standard error of the regression. For the disk-meters and plate-meters was the influence of season reported by several researchers (POWELL 1974, CASTLE 1976, STOCKDALE 1984, PALAZZO & LEE 1986, KARL & NICHOLSON 1987, BRYAN et al. 1989, REEVES et al. 1996), but VIRKAJÄRVI & MATILAINEN 1995 found no influence. The influence of varieties was reported as well for the disk-meters and plate-meters (SCRIVNER et al. 1986, PETERSON & HUSSEY 1987, BRYAN et al. 1989), but mostly neglected. Despite these results are the data not sufficient to determine the influence of different varieties or cultivation methods, but they are suitable to show the possible bias of the angle-biomass relationship due to these factors. The pooled year-to-year regression in winter rye shows a good degree of determination, but high standard errors of the regression. If possible, it is advisable to re-calibrate the sensor. KARL & NICHOLSON 1987 suggested pooled regressions for several years for the disk-meters and plate-meters, but most were calibrated, when measuring new fields, dates, varieties, and cultivation methods were encountered.
The influence of the biomass-height-ratio shows that the plant height with less tillers increases the angle of the pendulum more than a higher tiller density at a low plant height for an equal biomass value. This indicates that the bending distance is of high importance for the pendulum-meter.
The effect of stem inclination can be considered minor till BBCH 59 in rye and in wheat. The increase in difference between the measurements in the two opposite directions show the biasing
The effect of the depth of tramline results in a large error. With the here presented data, it is not possible to exclude the influence of the biomass on the change of angle by a change of the two pendulum parameters h_{A0} and h_{P}. The problem is not mathematical, but the deeper the tramline the lower is the cylindrical body, hence the cylindrical body touches lower parts of the stems, which have a different bending resistance (HITAKA 1968, SPIEWOK et al. 1970, SINGH & BURKHARDT 1974, SPIEWOK 1974, GOWIN 1980, SKUBISZ 1984, MÜLLER 1988, NIKLAS 1990, O’DOGHERTY et al. 1995).
Using a non-destructive sensor which is in contact with the measured item, it is important to know the limits for which the measurements are non-destructive. The pendulum parameter which was damaging the crop sooner or later is the mass of the pendulum m _{P}. Of the other two parameters, the height of the cylindrical body is, in the range of the tested parameters, not encountering a limit at which it proves destructive to the crop. The height of the pivot point is sometimes causing destruction, but at other times not. Table 27 shows the experienced limits at which destruction occurred for the parameters h_{P} and h_{A0}. For all three crops the destruction limit of the pendulum mass increases till BBCH 69.
Winter Rye
BBCH
32 39 59 69
m
_{P}
(kg)
2.5* >3.0* >3.0* 5*
h
_{P}
(m)
– – 0.4 0.8a
Winter Wheat
BBCH
34 39 59 69
m
_{P}
(kg)
2.0 2.0 5.0 4.0
h
_{P}
(m)
– – 0.4 0.5
Irrigated Rice
BBCH
25 39 49 65
m
_{P}
(kg)
1.5* 2.5* 2.5* 3*
h
_{P}
(m)
– – – 0.3 a = only in second year 1999
* without bad weather and black eye spot
Discussion
The limits of destructiveness show a difference between rice, wheat and rye. At no time is h_{A0} a parameter which can damage the plants, since it deviates out of the way. The parameter h_{P} is only at later growth-stages a destructive factor, usually if it is lower than about half the plant height. The destructive height of the pivot point can change with the years, but the ratio plant height to height of the pivot point is roughly the same in rye. Consequently is a parameter setting in one year well suited for biomass sensing while it is destructive in another year. The parameter m _{P} is at almost all growth-stages the factor having a destruction limit. The stems can carry only a specific amount before they buckle, usually named breaking force of the stem (HITAKA 1968, NIKLAS 1998), which is in some cases related to the weight of the stem (GARBER & OLSON 1919, DAVIS & STANTON 1932, ATKINS 1938a, ATKINS 1938b). The maximum breaking strength found by BARTEL 1937 supports the destructive limits of the parameter m _{P}. The limits at which m _{P} becomes destructive can be considerably lower, when bad weather conditions with rain and wind take place as in rice, and the limit drops to 0.5 kg for the entire crop, hence a sensing is impossible. The limits in rye can be as low as 0.5 kg for several stems, provided at least a medium infection with eye spot, but sensing of the crop is still possible.
Talking about a measurement system is so long incomplete as major sources of errors are not defined, and the defined errors have to be discussed by their distribution around the average. Everything influencing or changing the measurement and not being a part of the desired crop biomass is called error by definition. Random errors are distributed at random around an average, mostly in a Gaussian distribution, and have in their average no influence on the measurement.
Angle of deviation measured by the potentiometer,
Measurements of the slope sensor,
Speed measurements of the carrier speed,
Switch of the trigger,
Vibration of the pendulum.
The other class of errors is not normally distributed, but rather into one direction or one side, thus changing the measured average of the angle of deviation into one direction. They are called systemic errors, because they influence the measurement as long as the error itself is not measured to correct the biomass average. The following list gives important systemic errors:
Slope of the soil, if not measured and corrected,
Unevenness of the soil surface, resulting in a change of the tractor’s orientation,
Changes in the depth of the tramline,
Carrier speed, when not constant,
Accumulation of soil and litter in the weighted biomass,
Very strong winds,
Differences in wind direction towards the pendulum,
Lodging and stem inclination,
Variation of species composition in mixed crops,
Weeds and their patchy distribution,
Fungi diseases and stem insect pests occurring in patches.
The pendulum-meter most likely will transmit the bacterial diseases caused by Xanthomonas
Campestris, because some of the used inoculation techniques are similar to the contact measurement of the pendulum-meter, such as rubbing leaves with fingers (OU 1972, HOSSAIN et al. 1997). Therefore is the use of the pendulum-meter not advisable in regions with known incidences of bacterial diseases. Of the Southeast Asian rice production area is 8 % affected by bacterial leaf blight caused by Xanthomonas
Campestris (KHUSH & TOENNIESSEN 1991). But Xanthomonas
Campestris related diseases are also known in wheat systems (DUVEILLER
Following incidents were seen as obstacles, thus hindering or preventing the use of the biomass sensor pendulum-meter:
Infections with bacterial diseases caused by Xanthomonas campestris.
Rat (rattus rattus and rattus norvegicus) damage of the stem in rice.
Insect pests of the stem, such as the yellow stem borer (Scirpophaga incertulas), the striped stem borer (Chilo suppressalis), the dark-headed stem borer (Chilo polychrysus), the pink stem borer (Sesamia inferens), the white stem borer (Scirpophaga innotata), the South American white stem borer (Rupela albinella), and the wheat stem sawfly (Cephus cinctus Norton).
Root and stem fungi diseases such as eye spot (Pseudocercosporella herpotrichoides) in wheat and rye, and stem rot (Helminthosporium sigmoideum Catt.) and rice blast (Pyricularia oryzae Cav.) in rice.
Fungi diseases causing elongated growth such as bakanae (Gibberella fujikuroi (Sawada)) in rice.
Weather conditions that potentially produce lodging of cereal crops.
Severe or complete lodging in measured areas.
The so far presented results are focussing on the biomass sensor itself. Furthermore, the pendulum-meter is intended as a site-specific sensor, and for a site-specific biomass sensor it’s inescapable to look for its use in site-specific agriculture and site-specific crop protection. Biomass itself has rarely been a decision base for determining application rates in agriculture due to the difficulties of its determination in the field. An on-line biomass sensor, nevertheless, opens up a wide range of opportunities for the use of biomass data in decision making processes, because it is a rapid and easy tool while the crop is still growing. The rare use of biomass data as a parameter in crop cultivation, hence in site-specific plant protection, means that possibilities and potentials are mainly unexplored. Therefore are the here presented field trials only the first of many steps and trials to explore the use of biomass data as a decision base for a site-specific application of plant growth-regulators and fungicides. The present recommendations for fungicides and plant growth-regulators are based, among others, on crop density as in the decision support system PRO_PLANT. As long as there are no sensors for determining crop density, biomass data can bypass this bottleneck in site-specific plant protection. Unfortunately are BBCH 32 and 34 the earliest growth-stages in which the pendulum-meter can work in European cereals. Available growth-regulators are usually applied between the growth-stages BBCH 25 - 49, depending on the agent. Early applications of fungicides are sprayed at BBCH 25 - 29, intermediate between 31 and 49, while late applications are applied up to BBCH 69.
Lodging is a prime concern in cereal production. Though plant breeders have developed cultivars with stiff straw to reduce lodging potential, complete elimination of lodging, however, has not been achieved. Plant growth-regulators, such as MODDUS^{®} with the agent trinexapac-ethyl, form an integral part in crop management to minimise lodging in cereals. Regarding the measurement principle of the pendulum-meter, the biomass sensor is perfectly suited for directing different application rates of growth-regulators, though still not all factors that influence lodging and their magnitudes is known to calculate application rates.
To help in determining the effects of a site-specific differentiated application of the growth-regulator, the trial layout was divided into three tramlines, of which one was the uniformily treated tramline, one was the site-specific differentiated tramline, and the third was the control tramline without any treatment. The calculated averages of the angle of deviation of the biomass sensor are shown in figure 35, and the measurements in the two tramlines are similar though not identical. The green vertical flag lines in figure 35 indicate the change of the application rates
For determining the success of the application of plant growth-regulators, two measures can be taken into account. The first is the prevention of lodging and the reduction in plant height, and the second is the grain yield. The grain yield, each strip being weighted on a truck balance, showed little differences between the two variants, with 2.625 t ha^{-1} for the site-specific strip and 2.724 t ha^{-1} for the uniform test strip, both with 14 % grain moisture. Regarding the preventing of lodging and the reduction in plant height, it can be stated that the reduction in plant height was between 17 and 20 cm in the area of full application, compared to the control tramline, but without surprise there being no reduction for areas without application of growth-regulators. In the site-specific tramline, lodging was prevented, which was highlighted by the fact that lodging occurred in the neighbouring control strip as shown in figure 36.
This first field trial, with the use of a biomass sensor for site-specific differentiated application of growth-regulators, shows the potential usefulness of this approach, how an on-line real-time biomass sensor can be used in this relatively new field of precision farming.
The trial layout is standard in precision farming, but treating some tramlines with the common application rate, and the other ones with a site-specific reduced rate, faces always the problem of non-equally conditions between the tramlines. To my knowledge nothing is published about the relationship between biomass and the necessary application rate for growth-regulators, and scarce are the reports about the relationship between biomass and lodging. SHEEHY (IRRI, unpublished) reported that elite rice cultivars lodge above a grain yield of 10.5 t ha^{-1}. The artificial crop lodging caused by the pendulum-meter always occurred in plots with the highest biomass, except the stem buckling experienced by single stems. GONZALEZ et al. 1990 reported a biomass limit in grass stands, above which lodging happened, indicating that there is a limit of the biomass despite the bending resistance, above which lodging occurs.
Therefore, at the sites with a low or a medium angle of deviation, no growth-regulator was sprayed, while at the sites with medium to high biomass the full application was sprayed. Regarding the prevention of lodging, no plants lodged in the site-specific tramline as well as in the uniform tramline. This indicates that in the low biomass sites the growth-regulator was not necessary under the experienced weather conditions. In the neighbouring site of the control tramline, lodging occurred at the end of the tramline, where in the neighbouring site-specific tramline the highest angles of deviation were measured. For this site it can be stated that the high biomass needed growth-regulators though the result has to be repeated and the application rate for a specific angle of deviation has to be re-tested and clarified. The tested variety AMILO is listed in the German variety list BESCHREIBENDE SORTENLISTE 1998 as low to intermediate susceptible to lodging, further indicating the variability of lodging susceptibility due to the variety. This means different site-specific application rates for the same biomass when the variety is more or less susceptible to lodging. The application rate will be dependent on the future weather conditions of the crop, because bad weather is the principle cause of lodging (KONO & TAKAHASHI 1964, HITAKA 1968). The grain yield was almost the same, giving no reason to change the approach for the site-specific application of growth-regulators. The decision basis used by several researchers (SCHULZKE 1982, SCHÄDLICH et al. 1985, SCHÄDLICH et al. 1986, SCHULZKE & THIERE 1986, SCHULZKE et al. 1986) can not be transformed into an angle of deviation, since they used soil types and crop density as decision basis.
Lodging will occur in plants when the wind force applied onto the plant either exceeds the shear strength in the soil surrounding the roots or exceeds the strength of the stem. In addition to the wind’s force, any extra weight on the plant through rainfall interception or neighbouring stems damaged by diseases like eye spot and stem borers will increase the moment of the displaced stem.
As the top of the plant is displaced from the vertical, a second bending moment results from the force of gravity acting on the mass of the plant which is no longer directly over its supporting base, which illustrates a high similarity to the measurement with the pendulum-meter. The gravity term becomes important when the head of the plant accounts for a large part of the plants total weight, as is the ear or the panicle during ripening, highlighted by the fact that lodging occurs usually after heading and increases with grain filling.
Because the bending force is highest at the base of the cereal stem, the structural properties of these parts are most important in resisting the bending motion. Internal factors such as thickness of stem wall, stem diameter, and stem rigidity are therefore of high importance. External factors such as extra loading due to precipitation, reduced shear strength in wet soils, weakening of the stem due to the duration of bending, wind gust frequency, and the vibrations of the stem, as well as previous damaging due to fungi and insect diseases all enhance the probability of lodging. HITAKA 1968 stated a high correlation between bending moment of rice stems and their breaking strength. Fresh weight of the plant can increase 30 % during rainfall (HITAKA 1968), and changes in moisture content also affect bending resistance of the stem.
Reported agronomic factors associated with lodging and summarised by SCHÄDLICH & SCHULZKE 1986 are: amount of nitrogen, source of nitrogen, amount of seeds per area, soil types, soil moisture, temperature, crop density, growth-stage at the beginning of spring, duration of growth-stages, time of stem elongation.
The similarity between the measurement by the pendulum-meter and the wind force onto the plant is a major advantage for using this sensor in site-specific application of plant growth-regulators. This approach is already followed up by UDOH et al. 2000.
The use of the sensor for site-specific applications of plant growth-regulators will be limited to the intermediate and late application timings due to the impossibility of earlier measurements with the sensor. Site-specific application of plant growth-regulators will be focussed on temperate cereals since they are not applied in tropical rice and rarely used in European and American rice production.
Powdery mildew, caused by Erysiphe graminis DC. F. sp. tritici E. Marchal, is the most dominant fungi disease encountered in German cereal production. Disease severity is dependent on many factors, including cultural practices, variation in weather conditions, the level of cultivar susceptibility, and regional and in-field location. The disease may occur during all stages of growth.
To determine the effects of a site-specific differentiated application of fungicides, the trial layout was divided into two tramlines, of which one was the uniformily treated tramline, and the other one was the site-specific differentiated tramline according to the biomass sensor data.
The averages of the angle of deviation of the biomass sensor were calculated for every meter of the 320 meters long tramlines as given in figure 38. It was decided to either spray 33 %, or 66 % of the full application rate, or the full application of the recommended rate according to the measured angle of deviation. The green vertical flags in figure 38 indicate the change of the application rates between 0.33, 0.66 and 1.0 l ha^{-1} of JUWEL, or 100, 200 and 300 l ha^{-1} water respectively. These changes in application rate were determined according to the blue angle of deviation in figure 38 for the site-specific tramline. Spraying was based on the angle of deviation and not on the actual crop biomass. A zero application site was not included into that first field trial due to the lack of weather forecast data and future disease expectations.
As seen in figure 38 had the site-specific tramline three different sites: the first site with an angle of deviation of 35° - 45°, hence a low crop biomass, and an application of 0.33 l ha^{-1} of the fungicide in 100 l ha^{-1} water. The second site with an intermediate crop biomass, an angle of deviation of 40° - 50°, and an application of 0.66 l ha^{-1} of the fungicide in 200 l ha^{-1} water. And the third site with the highest crop biomass with an angle of deviation of 50° - 60° and full application of the recommended 1.0 l ha^{-1} of the fungicide. Also shown in figure 38 are the values of the average angle of deviation of those sites at the application time at BBCH 41, and the average percentage number of powdery mildew infected plants in the uniform tramline. The measured angle of the pendulum-meter proves the similarity in wide areas of the tramlines, but the selected tramlines are not as similar as in the growth-regulator trial.
For determining the success of site-specific fungicide treatment, the grain yield and the fungi infestation are the measure of success here. The grain yield shows small differences between the two variants, with 7.684 t ha^{-1} for the site-specific strip and 6.825 t ha^{-1} for the uniformily treated test strip, both for 14 % grain moisture.
The influence of crop biomass on disease incidence of powdery mildew, visible by the angle of deviation of the pendulum-meter, is illustrated in figure 39. In figure 39 is given the angle of deviation of the uniform tramline, measured at BBCH 41, together with the percentage of powdery mildew infected leaf area, measured at BBCH 75 in the same tramline. Here, the percentage of infected leaf area rises the higher the crop biomass is.
Discussion
Only a few reports exist regarding the potentials of site-specific application of fungicides. Those works were usually based on field assessment maps, some of them using leaf area indices to control site-specific differentiated application rates of fungicides. Due to a close relationship between leaf area and plant biomass, the biomass sensor pendulum-meter has a high potential for a site-specific on-line control of fungicide application rates. In this case, the presented field trial is a first step to introduce the biomass sensor pendulum-meter to the potentials of site-specific reduced application rates of the strobilurin-fungicide JUWEL in winter wheat.
This first field trial, with the use of a biomass sensor for site-specific differentiated application of fungicides, is not a sufficient base to determine the actual usefulness of this approach, but they show the way, how an on-line real-time biomass sensor can be used in this relatively new field of precision farming. As with the site-specific application of growth-regulators, the site-specific fungicide trial layout is standard in precision farming. First of all, a site-specific application of fungicides according to the pendulum-meter is possible and it is working. The grain yield is not differing much indicating that a reduced amount of the fungicide is sufficient to protect the crop. The slight yield differences may be explainable by the differences between the two tramlines although the tested tramlines were selected visually for the highest possible similarity. The
The second result is a strong influence of the biomass on the powdery mildew infestation. Thus proving that this approach of using biomass data for the site-specific application of fungicide against powdery mildew has a rational base.
To my knowledge nothing is known about the biomass-application rate relationship for the fungicide application. Thus further research is necessary to find algorithms for this approach. The tested variety BATIS is listed in the German variety list BESCHREIBENDE SORTENLISTE 1998 as low to intermediate susceptible to powdery mildew. This means that site-specific application rates will have to change with different varieties. The bonitation in the control strip shows a relationship between biomass and disease incidence of powdery mildew, although this is dependent on the weather conditions. For powdery mildew a dependency on air humidity was stated (FRIEDRICH 1995). A dependency of the air humidity on the biomass seems likely, but wasn’t found in the literature.
A LAI-meter was successfully used (JORGENSEN et al. 1997, SECHER 1997) as a base for site-specific application of fungicides. According to CALVERO & TENG 1997 is the simulation model BLASTSIM.2 partly based on the leaf area index of rice to simulate and manage a rice blast (Magnaporthe grisea Barr. and Pyricularia grisea (Cooke) Sacc.) epidemic, thus being potentially applicable in site-specific matters. According to several reports (AASE 1978, WANJURA & HATFIELD 1985, DOBERMANN & PAMPOLINO 1995, RETTA & ARMBRUST 1995), the leaf area index or the leaf area is correlated with leaf mass, stem area related with stem mass, hence the plant surface with the crop biomass.
WARTENBERG & JÜRSCHICK 1995 found a high correlation between the plant surface and the culm length. By using the pendulum-meter’s angle of deviation, the fungicide can be applied according to the amount of biomass, according to LAI, or according to the leaf area as the prime application area.
Mainly unexplored is also the relationship of the biomass on most of the leaf infecting fungi diseases.
Since the used fungicide JUWEL is a systemic fungicide, it is necessary to discuss the spraying object. Several researchers have used leaf area as the prime object of spraying. Without doubt
Although systemic fungicides are intruding plants via leaves and roots, the actual object is the entire plant and not only its surface. Bearing in mind the use of levels of antibodies in human medicine, such as the level of a vaccine to ensure a successful vaccination, this means that the fresh mass of the plant is the proper object of systemic fungicide spraying to ensure the necessary level of the fungicide agent in the plant. In terms of units this level can be expressed by one or more parts of the fungicide agent per million parts of the plant PPM. Unfortunately, to my knowledge no publications can be found about the PPM relationship between fresh mass and systemic fungicide agents.
The use of the sensor for site-specific applications of fungicides will be limited to the intermediate and late application timings. Earlier measurements with the sensor between BBCH 25 and BBCH 31 are impossible in winter rye and winter wheat and will be left over for other sensors. Site-specific application of fungicides will be focussed on European cereals because they are widely applied in European cereal production. Site-specific fungicide application in tropical rice production and American cereal production systems will have a lower priority due to the lesser use of fungicides there.
The aforementioned results were all obtained with a pendulum-meter mounted on a hand-pushed research carrier. Trigger and rails were sufficient to record positioning. Under practical farming conditions neither trigger nor a hand-pushed carrier is acceptable. Only a tractor-based pendulum-meter with an automatic on-line recording of the angle of deviation together with the GPS positioning data is acceptable for farmers.
Therefore, the programming of the measurement software μ –meter Nextview was customised to a joint recording of the angle of deviation of the pendulum-meter together with the positioning data of a Trimble^{®} 132-GPS. A prototype of the tractor-based pendulum-meter was mounted on the backward three-point linkage. A real-time view of the prototype tractor-based pendulum-meter measurement is visible in the attached video. The measurement frequency was 1 Hz, a higher frequency was not possible with the present system. Figure 40 shows the 1 Hz point measurements of the angle of deviation of the tractor-based pendulum-meter, measured in the tramlines of the winter wheat field Baasdorf at growth-stage BBCH 39. The shown measurement points are original data, not averaged or reduced values.
In figure 40 the tramlines are easily visible through the measurement points. The dots have dark blue to light blue colour for lower measurement values, and light red to dark red colours for higher angles of deviation. The black line indicates the edge of the field. No biomass data was collected at that site, therefore is this map rather a measurement map than a biomass map.
The majority of the field shows light red dots, indicating angles of deviation between 62° to 70°. Several patches show dark red dots with angles of 70° to 78°. In the south-west corner of the field is a site with a low biomass measurement with angles of deviation between 46° and 62°, illustrated by the light and medium blue dots, and also a few dots with even lower angles. Though the legend gives negative values of the angle of deviation as well, they were rarely measured in the sites with low biomass where the pendulum was swinging strongly and a measurement frequency of 1 Hz could record the negative values with the same probability as the other values. Nevertheless, the important range of the measurement values are the angles of deviation between 46° and 78°. Those angles show approximately 100 % difference between the areas of low biomass and the areas of high crop biomass.
This map is in an off-line spraying mode the end-product, but in the intended and not yet realised on-line real-time spraying mode, with a sensor in front controlling a field sprayer while passing across a field, it will be a by-product for researchers and farmers to store and express the obtained information on biomass.
In the area of site-specific application of plant growth-regulators it might be possible to arrange two or three management zones within the field. The full application rate may be advisable at the sites with a high biomass, and a zero application at those with low biomass measurements. The application rate at sites with intermediate biomass can potentially be lower than the full application, but to what degree and with which success is unknown, nevertheless, the risk will increase.
The application rate for fungicide spraying can be grouped to the same management zones within the field as for the plant growth-regulator, but amount of fungicide agent will have to be determined at the field according to actual disease pressure within these management zones. These questions have to be investigated in future researches.
Discussion
The presented tractor-based pendulum-meter for on-line mapping of crop biomass is the final result of this work. In difference to the before described results, there are some changes between the research carrier and the tractor as a carrier for the pendulum-meter. The measurement frequency of 1 Hz is too low and will bias the measurements due to the vibration of the pendulum, causing at some points negative values. This will have to be technically changed to higher measurement frequencies and the before mentioned reduction methods to an average or median. Location of the pendulum and the antenna are different, but negligible in a field. Movements of the tractor are much more influencing on the pivot point than the movement of the research carrier, due to the lever arm and the high centre of gravity of the tractor.
The dotted lines of the measurements in the tramlines represent only the biomass within the tramline, and these measurement points are interpolated for the adjoining areas, although these areas might be different due to the sideways heterogeneity in the field. The areas with low or high biomass are clearly pronounced in the interpolated map, which will be the basis for an off-line treatment.
Provided the growth-rate is identical within the field and there are no differences in the growth-stage between the sites, then a 100 % difference in biomass will result in a 100 % difference of grain yield. Though the weather at later growth-stages will be mostly unknown at the time of measurement, actual biomass can be seen as yield potential as it is an important part of crop growth models. Crop growth models were already used to derive site-specific yield potentials and thus management information (BOONE et al. 1997, WERNER et al. 2000).
The integrated slope sensor can as well be used, in connection with the biomass maps and slope-climate models or CERES-models to test for future growth patterns due to north or south slopes (BERGOLD 1993) and water stress (MAIER 1993).
The stored information either as maps or as measurement files may be useful for the farmer to see for himself his management results, and politics might enforce these maps in environmentally critical sites. Additionally, these maps enable researchers to test on field-scales the dependencies between crop biomass and various diseases. These relationships were neglected so far due to the high amount of time and labour for the destructive sampling methods.
Despite the dependency of the biomass maps on the availability of GPS signals are the actual sensor-sprayer combinations not dependent on the GPS signals, because the sensor-sprayer combination acts as a unit in an on-line real-time application and application rate is based on the biomass data and not on the location within the field.
The bending resistance of the cereal stems is highly related to the mass of the stems, thus enabling the biomass sensor pendulum-meter to utilise this fact as a contact measurement to sense non-destructively site-specific cereal crop biomass. The sensor provides the opportunity to measure on-line as well as on-the-go the heterogeneity of the crop biomass, and there is good evidence that the obtained data can be used as a decision base for a site-specific on-line real-time application of plant growth-regulators and fungicides.
This work gives a proper understanding of the manner in which the pendulum-meter operates with regard to the cereal plants, although calibration is advisable before measuring. Several factors have shown an influence on the sensory measurements, such as wind, weeds, rapid crop growth and differences in plant development within the field, stem and root fungi diseases, depth of tramline, variety, year and season, plant height, and stem inclination. In the here presented work it was only possible to identify these factors, but the magnitude of their influence of the measurements remain unexplored. Determination of this magnitude will have to be done in future test trials.
As the operation period is concerned, it has to be said that the pendulum-meter can not be used for the earlier applications in plant protection, neither for the early spraying of plant growth-regulator, nor for the early fungicide application. For these growth-stages, other sensors such as a NIR sensor or a “green” leaf reflectance sensor have to be developed and utilised as a decision base for site-specific applications of plant growth-regulators and fungicides.
Unfortunately, relatively little attention has been devoted to biomass, in terms of fresh mass or dry mass, as a decision base for site-specific matters, due to the usually destructive and time-consuming sampling. It therefore constitutes an unexplored parameter regarding the site-specific or general relationships between biomass and various characters, such as fungi development, lodging, soil fertility, crop density, and plant height. The here presented field trials clearly show that the measurements of the pendulum-meter can be used as a decision base for the site-specific application of growth-regulators and fungicides, although the relationship between lodging and biomass, and between most fungi and biomass needs further examination. Furthermore, the question arises about the proper amount of growth-regulators and fungicides whether they are sprayed according to biomass, according to leaf area index, or based on another method. With regard to plant growth-regulators, there appears to be a reasonably clear dependency of lodging on bending resistance of the cereal stem. As the results of this work indicate is the main cause for the deviation of the pendulum also the bending resistance. Consequently can be assumed that the
In difference to the site-specific application of plant growth-regulators is the site-specific fungicide spraying not related to a factor associated with the measurement principle. The here used approach for site-specific fungicide applications is based on two generalisations, of which the first is a close relation between biomass and leaf area as the prime spraying object. The second is that the fresh mass itself is the prime spraying object for systemic fungicides, assuming that the fungicide needs a specific level in the plant for successfully defeating the fungi disease, as it is successfully used in human medicine for a long time with regard to for example vaccination. Both approaches face the problem of calculating the proper amount of fungicide agent for successfully defeating the disease, bearing in mind that most fungi developments are dependent on weather conditions and a reduced application rates is always an increase in risk. To successfully manage the increased risk a co-operation with decision support systems, such as PRO_PLANT, crop models and weather forecasts would help implementing a sensor system for a site-specific fungicide application, whatever sensor that would be. As with site-specific applications of plant growth-regulators faces the pendulum-meter based site-specific fungicide spraying the problem that the pendulum-meter is not operating at early fungicide applications, hence other sensors have to be developed for that purpose.
It follows from an on-line sensor that the field sprayers can spray on-line and in real-time the wanted amount of spraying agent. A direct consequence is a change in the data processing for the field sprayer and technical modifications of the field sprayer to rapidly alternate application rates.
Further research areas are site-specific fertilization according to biomass data, yield mapping of whole plant silage, biomass mapping for determining N-losses and N-return. With regard to pastures and grasslands the pendulum-meter can replace the disc-meters and plate-meters for sensing grass yield, arranging of management zones in pastures according to pasture yield, and site-specific fertilization of pastures and grasslands.
The machine-based biomass sensor pendulum-meter can determine on-line the site-specific differentiation of cereal crop fresh masses and dry masses for the most growth-stages in winter wheat, winter rye, and irrigated rice. The pendulum-meter is well suited in wheat and rye for the growth-stages BBCH 39 to BBCH 69, and to a lesser degree for BBCH 34 and 32. Irrigated rice crop biomass was well sensed by the pendulum-meter at all tested growth-stages BBCH 25 to BBCH 65. Earlier growth-stages were not possible to measure. The angle-force relation of the pendulum-meter is non-linear. The most important factor for this contact measurement was found in the bending moment of resistance of the stems. For the reduction of the original data to representative plot values, the average of the angle, the average of the vector, and the median of the angle were suitable. The results of the optimisation trials, in terms of repeatability of the measurement and the accuracy of biomass determination, showed a wide range of suitable parameter settings and not a single optimal parameter. Nevertheless, the mass of the pendulum m _{P} should be low to avoid destruction of the plants, the height of the cylindrical body h_{A0} should touch the stems to ensure bending contact, and the height of the pivot point h_{P} should be at crop height to get a wide range of angles of deviation for a given range of biomass. For every growth-stage, the optimum h_{P}, h_{A0}, and m _{P} are different, and although a single pendulum-meter setting for all growth-stages is possible, but lacking good accuracy of biomass determination in many growth-stages. Without calibration the pendulum-meter still senses well the relative distribution of the site-specific biomass, hence the field heterogeneity. When only the crop’s heterogeneity is of interest, all pendulum settings can be used without calibration. The replicates with the same pendulum parameter settings show a standard deviation of the plot average of less than 1°, mostly less than 0.3° for the most growth-stages. The coefficient of variation is mostly less than 5 %, often less than 2 %, and it is higher the smaller the range of measured angles is. The accuracy of biomass determination of the pendulum-meter showed mostly R^{2} of 0.90 or higher. The linear regression performed with slightly lower R^{2} than the square, except for rye, where the square regression was much better for the late growth-stages. Solely in rye the plotted residuals showed a square bias. The standard errors of the regressions were less than 2 in rice, less than 3 in wheat, and less than 4 in rye, increasing during crop growth.
The multiple and simple regression for the dependency of the measured angle on the parameter settings of the pendulum was strongly influenced by the biomass thus causing a re-calibration when changing the pendulum parameters.
Site-specific application of growth-regulators and fungicides according to the measurements of the pendulum-meter, and hence the biomass, has been successfully conducted. The measurements of the pendulum-meter can be used as a decision base for the site-specific application of growth-regulators and fungicides, although the relationship between lodging and biomass, and between most fungi and biomass needs further examination, as well as determining the necessary amount of growth-regulators and fungicides according to biomass.
The biomass sensor pendulum-meter is suitable for controlling the intermediate and late applications of growth-regulators and fungicides, here BBCH 32 – 59, but not for the early applications, here BBCH 25 – 31, due to impossibility of using the sensor in low plants. In fungicide application, the sensor can be used similar to LAI, and in growth-regulators sprayings, the sensor has in principle a high similarity with lodging resistance.
Der maschinengestützte Pflanzenmassesensor „Pendulum-Meter“ kann online die teilflächenspezifischen Differenzierung der Bestandesfrischmassen und –trockenmassen der meisten Wachstumsstadien von Winterweizen, Winterroggen und Naßreis bestimmen. Das Pendulum-Meter ist in Weizen und Roggen sehr gut geeignet für die Stadien BBCH 39 bis 69, und mit geringerer Genauigkeit auch für die Stadien BBCH 32 bis 34. In Naßreis wurde die Biomasse in allen getesteten Wachstumsstadien von BBCH 25 bis BBCH 65 gut erfaßt. In früheren Wachstumsstadien ist eine Messung nicht möglich. Die Kraft-Winkel-Beziehung des Sensors ist nicht linear. Der wichtigste Faktor für diese Kontaktmessung ist der Biegewiderstand der Getreidehalme. Zur Reduzierung der Rohdaten zu repräsentativen Werten für die Parzellen sind sowohl der Mittelwert des Auslenkungswinkels und der Mittelwert des Vektors, als auch der Median des Auslenkungswinkels geeignet. Die Ergebnisse der Optimierungsversuche in
Die multiple und einfache Regression der Abhängigkeit der Meßwerte von den Parametereinstellungen des Sensors wurde stark von der Pflanzenmasse der Parzellen beeinflußt, weshalb eine Kalibrierung notwendig ist, wenn die Einstellungsparameter geändert werden.
Die Geländeneigung lenkt das Pendel aus, ohne einen Getreidebestand zu messen, und benötigt eine Korrektur durch einen Neigungssensor. Die Fahrgeschwindigkeit muß konstant gehalten werden, da der gemessene Auslenkwinkel eine starke Abhängigkeit von der Geschwindigkeit zeigt, aber gleichzeitig auch von der Menge der Pflanzenmasse. Der Biomassesensor kann die Bestandesdichte bestimmen, wenn die Bestandeshöhe homogen ist, und bestimmt die Bestandeshöhe, wenn die Bestandesdichte konstant ist. Wind mit niedriger Geschwindigkeit ist
Die Messungen des Biomassesensors können als Entscheidungsbasis zur teilflächenspezifischen Applikation von Wachstumsregeln und Fungiziden dienen, wenn auch die Entscheidungskriterien noch erarbeitet werden müssen.
Teilflächenspezifische Applikationen von Wachstumsreglern und Fungiziden nach den Messungen des Pendulum-Meters, also nach der Pflanzenmasse, sind erfolgreich durchgeführt worden. Die Messungen des Pendulum-Meters können dabei als Entscheidungsbasis für die teilflächenspezifische Applikation von Wachstumsreglern und Fungiziden benutzt werden, obwohl die Beziehung zwischen Lager und Pflanzenmasse, und zwischen den meisten Schadpilzen und der Biomasse weitere Untersuchungen erfordert, genauso wie die Ermittlung der notwendigen Aufwandmenge an Wachstumsreglern und Fungiziden je nach Pflanzenmasse.
Der Biomassesensor Pendulum-Meter ist für Kontrolle der mittleren und späten Applikationen von Wachstumsreglern und Fungiziden geeignet, hier die Stadien BBCH 32 – 59, aber nicht für die frühen Applikationen, hier die Stadien BBCH 25 – 31, wegen der fehlenden Eignung den Sensor in niedrigem Pflanzenbestand zu benutzen. In der Fungizidapplikation kann der Sensor ähnlich den LAI-Meßgeräten benutzt werden, und in der Ausbringung von Wachstumsreglern hat der Sensor eine große Gemeinsamkeit mit dem Widerstand gegen das Lagern.
El sensor automático de biomasa denominado pendulómetro (“pendulum-meter”) puede hacer una diferenciación sitio-específica de masa fresca y seca en cereales, y trabaja adecuadamente para la mayoría de los estadíos de crecimiento en trigo y centeio de invierno, así como en arroz irrigado. Para trigo y centeio de invierno, el “pendulómetro” es apropiado para los estadíos de crecimiento comprendidos entre BBCH 39 y BBCH 69, con poco menor precisión para los estadíos de BBCH 32 a BBCH 34. En arroz irrigado, el sensor trabajó bien para estimaciones de biomasa en todos los estadíos probados (de BBCH 25 a BBCH 65). No es posible hacer mediciones con el pendulómetro antes. La relación ángulo-fuerza del pendulómetro no es lineal. El factor más importante para este medición de contacto es la resistencia flexional de los tallos.
Las regresiones múltiple y simple para la dependencia de las mediciones de ángulos sobre los parametros del sensor fueron fuertemente influenciados por la masa vegetal, por lo cual es necesita una recalibración cuando se cambian los parámetro del péndulo.
La pendiente del terreno transforma el ángulo del pendulómetro, por lo que se necesita una corrección usando un sensor de inclinación. La velocidad del vehículo debe ser constante durante la medición, porque la desviación del ángulo es fuertemente dependiente de la velocidad del vehículo, e igualmente tambien depende de la cantidad de biomasa. El sensor del pendulómetro puede determinar la densidad de los tallos, cuando la altura de las plantas es homogénea, y puede determinar la altura de las plantas, cuando la densidad de tallos es constante. El viento de baja
La aplicación sitio-específica de reguladores de crecimiento y fungicidas respecto a lasmediciones del pendulómetro, y así la biomasa, fue realizado con éxito. Lasmediciones del pendulómetro puede ser usadas para la toma de decisiones sitio-específicas respecto a la aplicación de reguladores de crecimiento y fungicidas, aun quando la relacion entre la almacenamiento de los tallos y la biomasa, y entre la mayoría de las micosis y la biomasa, necesita más investigaciónes, así como la calculación de la cantidad necesaria de reguladores de crecimiento y fungicidas respecto a la biomasa.
El sensor de biomasa pendulómetro puede ser usado para controlar las medianas y posteriores aplicaciónes de reguladores de crecimiento y fungicidas, aqui los estadíos de BBCH 32 a BBCH 59, pero no puede ser usado para controlar las aplicaciónes temprano, aqui los estadíos de BBCH 25 a BBCH 31, por causa de la incapacidad del sensor para medir plantas bajas. En las aplicaciónes de fungicidas, el sensor puede ser usado como las mediciones de la superficie foliar, y en las aplicaciónes de reguladores de crecimiento, el sensor tiene por pricipio una grande semejanza con la resistividad contra la almacenamiento de los tallos.
O sensor automático de biomassa denominado pêndulometro (“pendulum-meter”) pode fazer uma diferenciação sitio-específica de massa fresca y seca em cereais, e trabalha adecuado para a maioria das fases de crecimento em trigo e centeio de inverno, assim como em arroz irrigado. Para trigo e centeio de inverno, o “pêndulometro” é apropiado para las fases de crecimento compreendidos entre BBCH 39 e BBCH 69, com poco menor precisão para las fases de BBCH 32 a BBCH 34. Em arroz irrigado, o sensor trabalha bem para estimações de biomassa em todas las fases probadas (de BBCH 25 a BBCH 65). Não é possível fazer medições com o pêndulometro antes. A relação ángulo-força do pêndulometro não é lineal. O factor mais importante para esta medição de contacto é a resistência flexão dos talos. Para a transformação dos dados originaies a um valor representativo para a parcela, os parámetros mais apropiados foram a media e a mediana do ángulo, e a media do vector. Os resultados das probas de
As regressões múltiplo e simples para a dependência das medições de ángulos sobre os parametros do sensor foram fortemente influênciados por a massa vegetal, por esso qual é necessário uma recalibração quando se cambiam os parámetros do pêndulo.
O pendor do terreno transforma o ángulo do pêndulometro, por esso é necessário uma correcção usando um sensor de inclinação. A velocidade do veículo deve ser constante durante a medição, porque a desviação do ángulo é fortemente dependente da velocidade do veículo, e igualmente também depende da quantidade de biomassa. O sensor pêndulometro pode determinar a densidade dos talos, quando a altura das plantas é homogénea, e pode determinar a altura das plantas, quando a densidade de talos é constante. O vento de baixa velocidade não é um factor de sesgo, mas a medida se incrementa o vento, o sesgo aumenta. Demais factores de sesgo podem ser a presença de ervas má, o crecimento rapido das plantas, a inclinação dos talos, a profundidade da rodada entre as plantas, e a variedade, por esso qual se requere recalibração do
A aplicação sitio-específica de reguladores de crecimento e fungicidas respeito às medições do pêndulometro, e assim a biomassa, foi realizado com êxito. As medições do pêndulometro pode ser usadas para a toma de decisões sitio-específicas respeito à aplicação de reguladores de crecimento e fungicidas, ainda que a relação entre o depósito dos talos e a biomassa, e entre a maioria dos fungos e a biomassa, necesita mais investigações, assim que como o cálculo da quantidade necessária de reguladores de crecimento e fungicidas respeito à biomassa.
O sensor automático de biomassa pêndulometro pode ser usado para revisar as medianas e posteriores aplicações de reguladores de crecimento e fungicidas, aqui as fases de BBCH 32 a BBCH 59,mas não pode ser usado pararevisar as aplicações temporão,aqui as fases de BBCH 25 a BBCH 31,por causa da incapacidade do sensor para medir plantas baixas. Nas aplicações de fungicidas, o sensor pode ser usado como asmedições dasuperfíciefolhado, e nasaplicação de reguladores de crecimento, o sensor háem pricípioumagrandesemelhança com aresistênciacontra adepósito dos talos.
° Degree ∆ Smallest still detectable differences in the measurement values α Angle of deviation of the pendulum meter given in degrees µ_{G}
Friction coefficient between two surfaces a Intercept of the regression equations a Acceleration ATB Institute of Agricultural Engineering B Fixed side of the cantilever in equation 1, here the root B Position of undeviated cylindrical body B’ Position of deviated cylindrical body BBCH Uniform code for development stages of arable crops bx Linear regression coefficient CV Coefficient of variation cx^{2}
Square regression coefficient DAS Days after seeding in rice DAT Days after transplanting in rice DGPS Differential global positioning system di Inner diameter of the stem DM Dry mass do Outer diameter of the stem e Height of the centre of gravity E Elasticity of the material, here Young’s modulus EI Bending rigidity of the stem F Force F_{A}
Force of the pendulum at 90° deviation F_{F}
Friction force F_{H}
Horizontal force component of the pendulum FM Fresh mass F_{P}
Force of the pendulum F_{R}
Resultant force F_{V}
Vertical force component of the pendulum G Gravity point or centre of gravity of a body GPS Global positioning system
Height of the cylindrical body of the pendulum h_{P}
Height of the pivot point of the pendulum h_{Pl}
Height of plants I Second moment of the area IDW Inverse distance weighted (interpolation method) IR 64 Paddy rice, IRRI-released variety 64 IR 72 Paddy rice, IRRI-released variety 72 IRRI International Rice Research Institute J_{B}
Mass moment at the fixed side B of a cantilever J_{G}
Mass moment in the gravity point G K Potassium LAI Leaf area index lin. Linear l_{P}
Length of the pendulum
m
Mass of a body max. Maximum min. Minimum
m
_{P}
Mass of the pendulum at 90 degree deviation
m
_{S}
Mass of the stems including mass moment of inertia n Sample size in equation 8 n Number of measurements to be reduced in equation 9 N Nitrogen NDVI Normalised difference vegetation index n_{S}
Number of stems P Pivot point P Phosphorus p Page PPM Parts per million r Height of the contact point between pendulum and stem R^{2}
Goodness of fit R_{Mb}
Bending moment of resistance s Standard deviation in equation 8 from KÖHLER et al. 1992 SD Standard deviation SE Standard error of estimate (in this work standard error of the regression)
Average of the vector v_{D}
Driving velocity WGS 84 World geodetic system of 1984 x Bending distance of the stem x_{i}
Length of the horizontal vector component y Dependent variable in the regression equation, here the angle of deviation y_{i}
Length of the vertical vector component
This work has been financed by the Hochschulsonderprogramm III (HSP III). The research in irrigated rice have been co-funded by the Gesellschaft für Technische Zusammenarbeit GTZ and the International Rice Research Institute IRRI.
Special thanks are due to my doctor father Prof. Hahn for his advice and fairness.
Considerable thanks belong to Mr. Witzke and Mrs. Grothe for their support with the Excel Visual Basic macro.
Special thanks to Dr. Bell from IRRI for supporting this international co-operation between ATB and IRRI, and many thanks to the colleagues from Agricultural Engineering Division AED and the Research Farm RF at IRRI. I enjoyed a lot working at AED and my stay at IRRI.
Last thanks, but not the least, belong to my great colleagues at the Division Engineering for Crop Production at the Institute of Agricultural Engineering ATB.
Video of Tractor-Based Measurement
Winter Rye
1998 / 1999
Königsfeld
Variety: AMILO
Soil types:
sandy loam, loamy sand
Preceding crop:
Winter rye
Date
Name
Application rate
Seeding
19^{th} September 1998 AMILO 120 kg ha^{-1}
Fertilizing
19^{th} March 1999 21 / 21 / 21 N P K 88 kg ha^{-1} nitrogen
Growth-regulator
3^{rd} May 1999 MODDUS 0.6 l ha^{-1}
Winter Wheat
1997 / 1998
Golzow
Field 58 Variety: ZENTOS
Soil types:
sandy loam, loamy sand, clayey loam
Preceding crop:
Maize
Date
Name
Application rate
Seeding
19^{th} October 1997 ZENTOS 199 kg ha^{-1}
Fertilizing
15^{th} March 1998 Ammoniating solution 71 kg ha^{-1} nitrogen 21^{st} April 1998 Ammoniating solution 56 kg ha^{-1} nitrogen 11^{th} May 1998 Calcium-ammonium-nitrate 41 kg ha^{-1} nitrogen
Herbicide
15^{th} March 1998 CONCERT 0.06 kg ha^{-1}
21^{st} April 1998 RALON 1.0 l ha^{-1}
21^{st} April 1998 HOESTAR 0.02 kg ha^{-1}
Growth-regulator
21^{st} April 1998 CCC 720 2.0 l ha^{-1}
Irrigated Rice
Variety: IR 72
Research Farm,
IRRI, Philippines, 1998 / 1999, monsoon season
Preceding crop:
Irrigated rice
Date
Name
Application rate
Seeding
16^{th} October 1998 IR 72 60-110 kg ha^{-1} differentiated
Fertilizing
14^{th} October 1998 Urea 60 kg ha^{-1} nitrogen
Weeding
4^{th} January 1999 By hand -
Winter Wheat
1999 / 2000
Baasdorf
Variety: CONTRA
Soil types:
sandy loam, loamy sand, clayey loam
Date
Name
Application rate
Seeding
16^{th}-20^{th} Oct. 1999 CONTRA 175 kg ha^{-1}
Fertilizing
3^{rd} April 2000 Ammoniating solution 100 kg ha^{-1} nitrogen 18^{th} May 2000 Calcium-ammonium-nitrate 68 kg ha^{-1} nitrogen
Herbicide
24^{th} March 2000 BACCARA 0.6 l ha^{-1}
Fungicide
10^{th} May 2000 ZENIT-M 0.35 l ha^{-1}
30^{th} May 2000 JUWEL TOP 0.7 l ha^{-1}
Growth-regulator
17^{th} April 2000 CCC 1.0 l ha^{-1}
Pendulum, Tested in Rice
l
_{P}
(m)
0.2 0.3 0.4 0.5 0.6
Vibration time (s)
0.86 1.07 1.26 1.42 1.56
l
_{P}
(m)
0.7 0.8 0.9 1.0 -
Vibration time (s)
1.68 1.78 1.88 1.92 -
Pendulum, Tested in Winter Rye and Winter Wheat
l
_{P}
(m)
0.2 0.3 0.4 0.5 0.6
Vibration time (s)
0.87 1.08 1.27 1.41 1.57
l
_{P}
(m)
0.7 0.8 0.9 1.0 1.1
Vibration time (s)
1.71 1.79 1.92 2.02 2.10
Pendulum
Fresh Mass
Dry Mass
Pendulum
Standard
Coefficient
h
_{P}
h
_{A0}
m
_{P}
linear
square
linear
square
mean
min.
max.
mean
min.
max.
mean
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
(°)
(°)
(°)
(°)
(°)
(%)
0.300 0.100 1.000 0.65** 3.73 0.66** 0.59** 4.01 0.64** 44.5 37.1 55.6 1.13 0.14 3.06 2.55 0.400 0.100 1.000 0.58** 3.86 0.63 * 0.52** 4.10 0.61 * 37.1 30.3 47.7 0.36 0.10 0.86 0.98 0.500 0.100 0.574 0.51** 3.20 0.55 * 0.46 * 3.38 0.54 * 39.4 34.3 47.8 0.18 0.07 0.32 0.47 0.500 0.100 1.000 0.49 * 3.54 0.54 * 0.43 * 3.76 0.52 * 34.0 29.0 42.8 0.19 0.12 0.30 0.56 0.500 0.100 1.500 0.52** 3.21 0.54 * 0.46 * 3.42 0.52 * 29.3 24.1 38.1 0.29 0.13 0.46 0.98 0.500 0.100 2.000 0.49 * 3.29 0.52 * 0.43 * 3.50 0.48 † 25.6 20.5 34.6 0.53 0.36 1.09 2.08 0.500 0.200 1.000 0.55** 3.81 0.55 * 0.50 * 4.04 0.51 * 14.9 9.0 26.7 0.37 0.18 0.82 2.52 0.600 0.100 1.000 0.45 * 3.17 0.50 * 0.38 * 3.35 0.47 † 32.2 28.2 39.5 0.51 0.34 0.80 1.58 0.700 0.100 1.000 0.53** 3.03 0.53 * 0.48 * 3.21 0.49 * 12.5 7.7 21.6 0.32 0.13 0.44 2.55 0.800 0.100 1.000 0.51** 2.88 0.51 * 0.45 * 3.04 0.46 † 12.5 8.4 21.0 0.38 0.23 0.55 3.03 0.900 0.100 1.000 0.50** 2.63 0.50 * 0.44 * 2.78 0.46 † 11.9 8.4 19.9 0.34 0.13 0.48 2.85 1.100 0.100 1.000 0.49 * 2.40 0.51 * 0.43 * 2.54 0.48 † 12.5 8.5 19.6 0.43 0.24 0.71 3.41 1.100 0.200 0.676 0.01 † 2.57 0.01 † 0.00 † 2.58 0.03 † 4.0 -1.3 9.2 0.68 0.46 0.92 16.95 ** Significance > 0.99 * Significance > 0.95 † not significant ٪ no replicates
Parameters
versus Angle
versus Angle
Angle
Deviation
of Variation
Pendulum
Fresh Mass
Dry Mass
Pendulum
Standard
Coefficient
h
_{P}
h
_{A0}
m
_{P}
linear
square
linear
square
mean
min.
max.
mean
min.
max.
mean
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
(°)
(°)
(°)
(°)
(°)
(%)
0.400 0.200 0.522 0.92** 6.19 0.99** 0.93** 5.87 0.99** 53.8 10.6 76.5 0.44 0.19 0.73 0.81 0.400 0.200 1.000 0.95** 4.57 0.99** 0.95** 4.34 0.99** 43.4 5.5 66.3 0.57 0.24 0.82 1.31 0.400 0.200 2.000 0.98** 2.16 0.99** 0.98** 2.17 0.99** 30.7 2.0 52.6 0.71 0.40 1.11 2.32 0.400 0.200 3.000 0.99** 1.35 0.99** 0.99** 1.52 0.99** 23.5 0.8 43.3 0.74 0.17 1.37 3.14 0.500 0.200 0.549 0.93** 5.16 0.99** 0.94** 4.88 0.99** 46.3 7.8 67.6 0.27 0.14 0.40 0.58 0.500 0.200 1.000 0.95** 4.02 0.99** 0.96** 3.80 0.99** 38.2 3.8 59.7 0.31 0.12 0.60 0.80 0.500 0.200 2.000 0.98** 2.19 0.99** 0.98** 2.28 0.98** 27.2 0.8 47.6 0.44 0.27 0.64 1.62 0.500 0.200 3.000 0.98** 1.80 0.98** 0.98** 1.99 0.98** 20.7 -0.1 39.1 0.51 0.19 0.88 2.48 0.500 0.300 0.522 0.98** 3.21 0.98** 0.97** 3.44 0.98** 35.4 1.0 61.8 0.48 0.20 1.04 1.36 0.500 0.300 1.000 0.97** 2.97 0.97** 0.96** 3.38 0.97** 25.9 0.0 50.5 0.55 0.26 1.09 2.13 0.500 0.300 2.000 0.95** 2.80 0.97** 0.94** 3.14 0.97** 16.1 -1.2 36.4 0.41 0.19 0.66 2.58 0.500 0.300 3.000 0.94** 2.45 0.98** 0.93** 2.69 0.97** 11.3 -1.4 28.2 0.44 0.25 0.61 3.86 0.800 0.200 0.615 0.94** 3.56 0.98** 0.95** 3.38 0.98** 34.3 5.6 51.3 0.38 0.23 0.76 1.10 0.800 0.200 1.000 0.95** 3.28 0.98** 0.95** 3.14 0.98** 29.4 1.9 46.1 0.35 0.15 0.66 1.20 0.800 0.200 2.000 0.96** 2.49 0.98** 0.96** 2.46 0.97** 21.8 -1.4 37.4 0.40 0.12 0.69 1.84 0.800 0.200 3.000 0.97** 2.01 0.97** 0.97** 2.06 0.97** 17.4 -2.1 31.7 0.36 0.14 0.79 2.09 0.800 0.300 0.595 0.97** 2.44 0.97** 0.97** 2.64 0.97** 20.1 -1.8 40.4 0.37 0.16 0.70 1.83 0.800 0.300 1.000 0.97** 2.63 0.97** 0.97** 2.69 0.97** 24.9 -1.1 45.5 0.37 0.09 0.59 1.47 0.800 0.300 2.000 0.95** 2.49 0.96** 0.94** 2.72 0.96** 13.2 -2.0 30.5 0.47 0.28 0.75 3.56 0.800 0.300 3.000 0.94** 2.22 0.96** 0.93** 2.38 0.96** 9.5 -1.5 23.8 0.42 0.23 0.60 4.41 1.100 0.200 0.676 0.94** 3.10 0.98** 0.94** 2.95 0.98** 29.2 5.7 43.2 0.35 0.18 0.66 1.20 1.100 0.200 1.000 0.94** 2.92 0.98** 0.95** 2.81 0.98** 26.0 3.4 39.9 0.30 0.13 0.49 1.16 1.100 0.200 2.000 0.96** 2.39 0.99** 0.96** 2.28 0.98** 21.7 1.6 35.2 1.11 0.43 2.13 5.12 1.100 0.200 3.000 0.97** 1.79 0.98** 0.97** 1.74 0.98** 18.0 0.7 31.0 0.43 0.17 1.10 2.42 1.100 0.300 0.657 0.97** 2.32 0.97** 0.96** 2.41 0.97** 20.7 0.6 36.9 0.56 0.33 1.09 2.70 1.100 0.300 1.000 0.96** 2.45 0.96** 0.95** 2.66 0.95** 17.5 -0.2 33.2 0.32 0.14 0.47 1.83 1.100 0.300 2.000 0.94** 2.36 0.95** 0.93** 2.57 0.95** 12.0 -1.5 26.6 0.37 0.21 0.70 3.05 1.100 0.300 3.000 0.93** 2.19 0.96** 0.92** 2.39 0.95** 9.3 -1.1 22.6 0.39 0.19 0.80 4.22 ** Significance > 0.99 * Significance > 0.95 † not significant ٪ no replicates
Parameters
versus Angle
versus Angle
Angle
Deviation
of Variation
Pendulum
Fresh Mass
Dry Mass
Pendulum
Standard
Coefficient of
h
_{P}
h
_{A0}
m
_{P}
linear
square
linear
square
mean
min.
max.
mean
min.
max.
mean
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
SE
R
^{2}
SE
R
^{2}
SE
(°)
(°)
(°)
(°)
(°)
(°)
(%)
0.500 0.200 0.549 0.85** 9.79 0.98** 3.60 0.80** 11.03 0.98** 3.74 57.7 5.3 80.4 0.22 0.12 0.39 0.38 0.500 0.200 3.000 0.94** 4.33 0.99** 1.88 0.91** 5.25 0.99** 1.68 30.0 -0.1 49.7 0.42 0.12 0.86 1.39 0.500 0.300 0.522 0.89** 9.03 0.98** 3.95 0.85** 10.48 0.98** 3.82 51.4 1.9 78.4 0.34 0.11 0.83 0.66 0.600 0.200 0.549 0.84** 9.24 0.98** 3.42 0.80** 10.33 0.98** 3.52 54.1 5.1 74.5 0.26 0.12 0.65 0.47 0.600 0.200 3.000 0.92** 4.62 0.99** 1.84 0.89** 5.48 0.99** 1.76 29.9 -0.5 48.5 0.40 0.21 0.58 1.35 0.600 0.300 0.549 0.87** 8.97 0.98** 4.02 0.83** 10.23 0.98** 3.76 47.5 0.3 71.8 0.29 0.14 0.44 0.62 0.700 0.200 0.574 0.85** 8.25 0.98** 3.12 0.81** 9.30 0.98** 3.22 50.3 5.8 69.2 1.22 0.73 2.27 2.43 0.700 0.200 3.000 0.91** 4.75 0.99** 1.86 0.88** 5.65 0.99** 1.94 29.6 -1.0 47.2 0.43 0.25 0.64 1.44 0.700 0.300 0.549 0.89** 7.67 0.98** 3.69 0.85** 8.86 0.98** 3.45 42.4 1.0 64.8 0.24 0.12 0.45 0.56 0.800 0.200 0.615 0.84** 7.85 0.98** 3.19 0.80** 8.82 0.98** 3.18 48.1 6.5 65.7 0.32 0.14 0.72 0.67 0.800 0.200 3.000 0.92** 4.59 0.98** 2.29 0.88** 5.45 0.98** 2.20 28.9 1.0 46.0 0.79 0.19 1.42 2.72 0.800 0.300 0.595 0.88** 7.21 0.97** 3.66 0.85** 8.27 0.98** 3.34 41.0 2.5 61.7 0.62 0.24 1.17 1.51 0.900 0.200 0.635 0.86** 7.10 0.98** 2.78 0.82** 8.02 0.98** 2.80 43.7 4.8 60.8 0.33 0.13 0.63 0.76 0.900 0.200 3.000 0.92** 4.12 0.98** 2.38 0.89** 4.92 0.98** 2.31 26.8 1.8 43.4 0.44 0.08 0.56 1.64 0.900 0.300 0.615 0.88** 6.69 0.97** 3.58 0.85** 7.70 0.97** 3.33 37.9 3.1 57.0 0.50 0.29 1.06 1.31 1.000 0.200 0.657 0.86** 6.49 0.98** 2.58 0.82** 7.34 0.98** 2.55 41.5 5.8 57.4 0.42 0.16 0.89 1.01 1.000 0.200 3.000 0.92** 3.86 0.98** 2.33 0.89** 4.59 0.98** 2.19 25.7 1.9 41.4 0.45 0.17 0.93 1.77 1.000 0.300 0.635 0.89** 6.10 0.96** 3.61 0.85** 7.00 0.97** 3.37 35.5 4.3 53.6 0.48 0.12 1.58 1.35 1.100 0.200 0.676 0.87** 6.04 0.98** 2.56 0.83** 6.88 0.98** 2.48 37.6 4.3 53.1 0.43 0.15 0.98 1.15 1.100 0.200 3.000 0.92** 3.67 0.97** 2.29 0.89** 4.35 0.98** 2.14 23.4 1.0 38.1 0.45 0.29 1.04 1.91 1.100 0.300 0.657 0.89** 5.52 0.96** 3.43 0.86** 6.40 0.97** 3.24 32.0 3.7 49.0 0.62 0.36 1.36 1.93 1.400 0.200 0.735 0.86** 5.40 0.98** 2.30 0.82** 6.15 0.98** 2.29 33.8 4.0 47.0 0.21 0.10 0.38 0.61 1.400 0.200 3.000 0.91** 3.52 0.98** 1.76 0.87** 4.15 0.98** 1.66 23.0 2.9 34.9 0.37 0.09 0.91 1.59 1.400 0.300 0.696 0.89** 4.71 0.97** 2.49 0.85** 5.52 0.97** 2.49 29.1 3.4 43.3 0.40 0.10 0.94 1.36 ** Significance > 0.99 * Significance > 0.95 † not significant ٪ no replicates
Parameters
versus Angle
versus Angle
Angle
Deviation
Variation
Pendulum
Fresh Mass
Dry Mass
Pendulum
Standard
Coefficient
h
_{P}
h
_{A0}
m
_{P}
linear
square
linear
square
mean
min.
max.
mean
min.
max.
mean
(m)
(m)
(kg)
R
^{2}
SE
R
^{2}
R
^{2}
SE
R
^{2}
(°)
(°)
(°)
(°)
(°)
(°)
(%)
0.800 0.200 0.615 0.78** 8.00 0.93** 0.77** 8.27 0.94** 65.3 26.6 80.6 0.28 0.11 0.65 0.57 0.800 0.200 1.000 0.79** 7.84 0.94** 0.77** 8.10 0.95** 59.3 21.4 74.8 0.29 0.18 0.50 0.59 0.800 0.200 1.500 0.78** 7.67 0.95** 0.77** 7.89 0.95** 52.7 16.5 67.6 0.28 0.06 0.48 0.66 0.800 0.200 2.000 0.81** 7.09 0.96** 0.80** 7.37 0.97** 51.4 16.2 66.0 0.36 0.17 0.60 0.74 0.800 0.200 2.500 0.82** 6.84 0.96** 0.80** 7.12 0.97** 47.9 13.5 62.3 0.28 0.11 0.40 0.74 0.800 0.200 3.000 0.76** 7.19 0.95** 0.75** 7.39 0.96** 43.3 11.0 56.0 1.14 0.64 2.56 3.02 0.800 0.200 3.500 0.82** 6.25 0.97** 0.81** 6.51 0.97** 40.6 9.5 54.6 0.30 0.13 0.47 0.99 0.800 0.200 4.000 0.83** 5.96 0.97** 0.82** 6.25 0.98** 38.2 8.2 52.4 0.25 0.11 0.41 0.91 0.800 0.200 4.500 0.84** 5.70 0.97** 0.82** 5.99 0.98** 35.9 7.1 49.7 0.35 0.08 0.68 1.51 0.800 0.200 5.000 0.84** 5.59 0.98** 0.82** 5.90 0.98** 35.7 7.1 49.4 0.54 0.14 2.20 2.02 1.000 0.300 1.000 0.87** 8.12 0.98** 0.92** 6.43 0.98** 62.2 22.6 80.3 0.59 0.33 1.05 0.95 1.100 0.100 1.000 0.21 † 16.83 0.60 * 0.28 † 16.09 0.53 † 61.8 34.1 79.2 10.49 0.15 102.38 16.98 1.100 0.200 1.000 0.87** 6.63 0.97** 0.92** 5.33 0.97** 63.7 31.2 78.7 0.41 0.23 0.55 0.65 1.100 0.300 0.657 0.87** 7.79 0.97** 0.92** 6.23 0.98** 65.1 27.4 82.6 0.32 0.12 0.47 0.49 1.100 0.300 1.000 0.87** 7.64 0.97** 0.92** 6.08 0.98** 60.4 23.5 77.0 0.40 0.21 0.85 0.67 1.100 0.300 2.000 0.87** 7.44 0.98** 0.92** 5.89 0.98** 49.2 13.4 65.8 7.78 3.43 9.19 15.81 1.100 0.400 1.000 0.87** 8.37 0.98** 0.92** 6.59 0.98** 55.3 14.9 74.0 2.00 1.12 3.80 3.62 1.100 0.500 1.000 0.89** 8.20 0.98** 0.93** 6.41 0.98** 51.2 10.3 71.4 2.81 2.19 3.51 5.50 1.100 0.600 1.000 0.91** 7.19 0.98** 0.95** 5.32 0.99** 45.8 5.5 67.0 2.69 0.65 4.36 5.87 1.100 0.700 1.000 0.93** 5.83 0.98** 0.97** 4.16 0.98** 41.3 4.0 63.0 3.04 0.23 5.16 7.35 1.100 0.800 1.000 0.96** 4.03 0.98** 0.98** 2.94 0.98** 36.0 3.4 59.0 2.33 0.35 4.14 6.46 1.100 0.900 1.000 0.95** 4.33 0.98** 0.97** 3.14 0.98** 43.4 11.8 63.6 14.38 10.27 19.52 33.14 1.200 0.300 1.000 0.86** 7.15 0.96** 0.91** 5.80 0.96** 58.1 24.0 73.6 1.21 0.46 1.56 2.08 1.300 0.300 1.000 0.42 * 17.01 0.45 † 0.44 * 16.74 0.46 † 51.8 18.0 71.0 10.34 0.43 97.80 19.97 1.400 0.300 1.000 0.86** 6.58 0.95** 0.90** 5.
Parameters
versus Angle
versus Angle
Angle
Deviation
of Variation