The long-term agrometeorological field experiment at Berlin-Dahlem, Germany

The long-term agrometeorological field experiment at Berlin-Dahlem, Germany

Agricultural and Forest Meteorology 96 (1999) 39±48 The long-term agrometeorological ®eld experiment at Berlin-Dahlem, Germany F.-M. Chmielewskia,*, ...

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Agricultural and Forest Meteorology 96 (1999) 39±48

The long-term agrometeorological ®eld experiment at Berlin-Dahlem, Germany F.-M. Chmielewskia,*, W. KoÈhnb a

Humboldt-University of Berlin, College of Agriculture and Horticulture, Institute of Plant Sciences, Subdivision of Agricultural Meteorology, Albrecht-Thaer-Weg 5, D-14195, Berlin-Dahlem, Germany b Humboldt-University of Berlin, College of Agriculture and Horticulture, Institute of Plant Sciences, Experimental Station, Albrecht-Thaer-Weg 5, D-14195, Berlin-Dahlem, Germany Received 5 October 1998; received in revised form 30 April 1999; accepted 25 May 1999

Abstract In order to investigate the relationships between atmospheric in¯uences and crop yields, long-term observations of meteorological and agronomic parameters are necessary. The agrometeorological ®eld experiment at Berlin-Dahlem (Germany), which was established in autumn 1952, is a unique basis for such studies. The original aim of this experiment was to show how the variability of weather affects the growth, development and yield formation of different crops. On eight plots, potatoes, winter rye, ®eld beans, oats, sugar-beet, maize, spring barley, and yellow lupin are grown on a permanent crop rotation. The crop management from year to year is constant so that the annual variability of crop yield is only the result of climatic ¯uctuations. In this paper, the long-term experiment is described and the observed crop yields are brie¯y discussed. Following papers will present results of relationships between atmospheric in¯uences and crop yields for spring and winter cereal, root crops and legumes in detail. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Weather; Crop yield; Long-term ®eld experiment

1. Introduction The performance of a cropping system depends on interactions between plant, soil and weather. Plant and soil factors can be modi®ed by human management to a considerable extent (breeding, choice of variety, plant protection, weed control, manuring and mineral fertilization etc.). However, there exist only limited *

Corresponding author. Tel.: 49-30-314-71210; fax: +49-30-31471211 E-mail address: [email protected] (F.-. Chmielewski)

possibilities to control or alter the effects of meteorological factors. Therefore, an important question in agricultural meteorology is Ð `How do atmospheric conditions in¯uence plant growth and plant development as well as yield formation', i.e. the development of yield determining components, and ®nal crop yield? Annual yield variability can be considerable and is a measure for yield stability of a particular location. Well adapted cultivars and species usually show smaller yield variations from year to year than less adapted ones. To investigate relationships between atmospheric parameters and crop yield a ®eld experiment must

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be established in which all factors concerning crop management (soil tillage, fertilization, choice of variety etc.) are constant, leaving weather as the only variable factor. In order to get statistically signi®cant results at least 30 years of meteorological and agronomic observations are required (Chmielewski, 1992; Chmielewski and Potts, 1995). Improved knowledge of the relationships between weather ¯uctuations and yield variability can help to assess possible impacts of climatic changes on agricultural production (HoÈrmann and Chmielewski, 1998). The increasing world population gives this problem additional importance. Previous crop/weather investigations, using the data of this long-term agrometeorological ®eld experiment, were carried out by Aster (1961) and HuÈnicken (1978). Aster used the data between 1953 and 1957 in order to detect relationships between meteorological factors and crop yields, applying a special method of Tamm (1952). HuÈnicken developed statistical models for an early agrometeorological crop yield assessment. Studies by RoÈmer (1988) focused on relations between phenophases of plants and weather conditions as well as the impact of phenophases on yield formation. Further investigations concentrated in the past on the following the subjects which will also be continued in the future:  Microclimatical investigations in different crop stands and their relation to the yield formation (Tamm and Funke, 1955; Tamm et al., 1956; Tamm et al., 1959).  Course of soil moisture under different crops (Kleineidam, 1965).  Investigation of water demand and water consumption of agricultural crops, estimates of actualevaporation on the basis of meteorological data, especially potential evaporation values (KoÈhn, 1984).  Simulation of parameters concerning the soil-water budget and the development and yield of plants.

2. The experimental site 2.1. The climate The experimental site, Berlin-Dahlem (528280 N, 138180 E, altitude 51 m), is located in the southwest

of the city. The climate is semi-continental and thus characterized by occasional cold winters (winters with chilling units Ð which is the sum of daily average air temperature below 08C over the period 1 December to 28/29 February Ð more than 2908C: 1953±54, 1955± 56, 1962±63 (coldest), 1969±70, 1984±85, 1986±87, 1995±96) and sometimes hot summers (summers with heating units Ð which is the sum of daily average air temperatures above 208C over the period 1 June to 31 August Ð above 1008C: 1969, 1971, 1975, 1976, 1982, 1983, 1992, 1994 (warmest), 1995, 1997). The absolute minimum air temperature was ÿ22.08C in 1956 and the maximum reached 37.58C in 1994. The average period without any frost normally extends from May to September. An exception was the year 1978 in which the latest frost was observed on 12 May. The earliest frost was 2 October (1957). The cropping season is between end of March (mean daily air temperature mostly >5.08C) and mid-November. For the 1961±90 standard period average annual air temperature was 9.38C (range 8.1±10.98C) and annual precipitation averaged 546 mm (range 356±736 mm), with maximum rainfall in June (70 mm) and August (64 mm). Since temperature is about 0.58C higher than in the surrounding areas, the site is slightly urban in¯uenced. The mean annual climatic water balance (precipitation minus potential evaporation calculated by Penman formula) is ÿ78 mm. Positive values in the long-term average only appear between October and February. Further information about the climatic conditions at Berlin-Dahlem is given in Table 1 and Fig. 1. 2.2. The soil According to FAO the soil at Berlin-Dahlem is classi®ed as an albic luvisol. The soil properties, especially in the subsoil, are very heterogeneous due to different processes of soil movement in the last ice age. The soil is a silty sand with about 72% sand, 25% silt and 3% clay in the topsoil (0±0.30 m). In the deeper layers, the clay content increases to more than 10%. Table 2 gives additional information about the physical soil parameters in the different layers to a depth of 0.80 m. The contents of humus and nutrients, with the exception of phosphorus, are only medium to low (Ct 560, Nt 52, P 14.9, K 12.3, Mg 3.8; each in mg per 100 g soil). The pH-value of 5.8 is common for this type of soil.

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Table 1 Monthly climatic data from the climate station at Berlin-Dahlem (standard period 1961±90), observations at 7 a.m., 2 p.m., 9 p.m Month

Air temperature (8C)

January February March April May June July August September October November December

ÿ0.1 0.9 4.3 8.7 13.8 17.1 18.5 18.0 14.3 9.9 4.9 1.4

Average/sum

9.3

Global radiation (MJ mÿ2) 61.0 114.3 240.0 363.1 513.1 532.7 521.5 446.4 298.1 170.6 72.4 43.9 3377

Saturation deficit (kPa)

Precipitation (mm)

0.10 0.13 0.23 0.40 0.60 0.70 0.75 0.67 0.39 0.24 0.15 0.10

37 30 32 38 53 70 53 64 43 33 45 48

9 13 35 61 97 109 111 91 52 25 13 8

28 17 ÿ3 ÿ23 ÿ44 ÿ39 ÿ58 ÿ27 ÿ9 8 32 40

0.37

546

624

ÿ78

3. Description of the long-term field experiment The agrometeorological ®eld experiment was established in 1953 by E. Tamm (Tamm et al., 1965). The objective of this experiment was to collect data needed to model the effects of atmospheric conditions on growth, development and yield of different crops.

Pot. Evapotranspiration by Penman (mm)

Climatic water balance (mm)

An automatic meteorological station was developed and installed in 1953 and has been extended and improved as new technology became available (Krzysch et al., 1984), latest revisions were in 1996. The agrometeorological ®eld consists of eight single plots of 214 m2 each, arranged around the weather station (Fig. 2). Potatoes, winter rye, ®eld beans, oats,

Fig. 1. Climate diagram after Walter and Lieth (1960) showing monthly temperatures and precipitation for 1961±90 standard period at BerlinDahlem. According to the suggestions by Walter and Lieth the absolute maximum and minimum of air temperature, the average maximum of the warmest and average minimum of the coldest months are given (values on the top and at the bottom of left axis) as well as months with an average temperature below zero (black bars on the time axis) and months with an absolute minimum below zero (hatched bars) are shown.

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Table 2 Physical soil parameters at the experimental site Parameter/layer (m)

0±0.30

0.30±0.50

0.50±0.80

Sand-content (%) Silt-content (%) Clay-content (%) Available field capacity (mm) Bulk density of dry soil (g cmÿ3)

72.1 25.0 2.9 58.8 1.72

69.8 25.5 4.7 35.9 1.80

65.3 23.4 11.3 45.3 1.80

sugar-beet, maize, spring barley and yellow lupins are grown on a permanent crop rotation. This means that each crop is repeated on the same plot after every 8 year. Management from year to year (e.g. crop rotation, mineral and manure fertilization, plant protection, soil tillage) has remained unchanged since the start of the experiment. Details of the agronomic management are shown in Table 3. With respect to current fertilizer recommendations, the application rates of NPK are relatively low. Varieties cultivated in the ®eld experiment had to be changed several times within the last 45 years (Table 4). As it was ensured that the type of variety (habit, development, yield formation) was unchanged, no discernible effect on yield level was observed. Extensive phenological observations are made, for example, for cereals the phenophases of emergence, two-leave-stage, three-leave-stage, beginning of tillering, shooting, heading, ¯owering and the ripeness stages like milk ripeness, wax-ripe stage and yellow ripeness are observed. Since 1966 also micro-phenological phases were included in the phenological

observation program. They describe the developmental stages at the vegetation cone and give information on the development of spikelet and ¯oret primordia. For the other crops, similar phenophases are recorded. During the whole growth period the developmental process and state of health is evaluated. Fig. 3 shows a cereal-plot in detail. There are two harvest areas (each 25 m2), two locations for destructive sampling (T1, T2) as well as sampling points S1±S10 (10 rowsegments of 2 m length), altogether used for the determination of several yield characteristics. In the centre of each plot are meteorological instruments used to measure the microclimate of the stand (air temperature and relative humidity 0.20 m above ground and the soil temperature at 0.20 m soil depth). Additionally, the three measuring points for soil moisture are shown. Destructive sampling is carried out each year before heading. The harvested plants are used to determine the weight of stems and leaves as well as leaf-area index at the end of the shoot growth. These data give some information on the in¯uence of weather on the yield formation up to this developmental stage. The row-segments S1±S10 are used to determine the different yield characteristics like the number of germinated plants and the crop density (ears/panicles per m2), the number of kernels per ear/panicle, the number of fertile and sterile spikelets as well as the kernel weight. The grain and straw yield as well as the hectolitre weight are ascertained from the two harvest areas. For the other crops, data in a comparable extent are observed. Table 5 shows the yield parameters gathered for cereal. The grain and

Fig. 2. Design of the agrometeorological field experiment.

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Table 3 Cropped species, average dates of sowing and harvesting (1953±96) as well as annual dressing Crop

Average date of sowing day/month

Average date of harvest day/month

N (kg haÿ1)

P (kg haÿ1)

K (kg haÿ1)

CaCO3 (t haÿ1)

Farmyard manure (t haÿ1)

Potato Winter ryea Field bean Oats Sugar-beet Maize Spring barleyb Yellow lupin

26/04 29/09 07/04 05/04 17/04 04/05 05/04 09/04

20/09 02/08 16/08 07/08 24/10 18/10 01/08 25/08

80 40 20 60 120 80 50 ±

28 21 21 21 28 21 14 21

100 50 83 66 133 100 50 66

1 ± 1 ± 1 1 ± ±

30 ± ± ± 30 30 ± ±

a b

Following catch crop (spring rape as green manure) with, 100 kg N haÿ1, 35 kg P haÿ1, 124 kg K haÿ1. Following catch crop (blue bitter-lupin and hairy vetch as green manure) with, 20 kg N haÿ1, 35 kg P haÿ1, 124 kg K haÿ1.

straw yields were measured beginning in 1953 and other parameters were added in 1962. The meteorological station is located in the centre of the ®eld on an uncultivated plot (Fig. 2). Between the plots the soil is covered by grass. The standard parameters such as solar radiation, air and soil-temperature, relative humidity, wind speed and wind direction are recorded automatically. From May to October the evaporation is measured by different evaporimeter types. All data are taken in a time scale of a few seconds and stored at every 15 min. Once a week, the soil moisture is measured under bare soil (base) and under the eight crops using stationary TDRprobes in three depths (Fig. 3). Daily climatic observations began at this site as far back as 1931 (climate station). Table 6 gives a survey of all meteorological parameters gathered at this site, mostly since 1953.

4. The crop yields between 1953 and 1996 Table 7 shows the average crop yields in the period 1953±96. Additionally, the extreme yields (Maximum, Minimum) and measures of variance (s, v) are given. In Figs. 4 and 5 crop yields between 1953 and 1996 are presented. Among the cereals, winter rye shows the highest yield stability (v = 16.7%). Also the yield components (ears/panicles/mÿ2, kernels per ear/panicle, kernel weight) have a smaller variability than those of the two spring cereals. The good water supply and moderate temperatures in winter time and early spring are very favourable for the growth and yield development of winter cereals in general (high rate of tillering, long period of spikelet and ¯oret formation). Moreover, the winter rye is well adapted to the sandy soil of this site.

Table 4 Varieties cultivated in the period 1953±96a Crop

Variety

Potato Winter rye Field bean

Ackersegen (1953±65), Cosima (1966±90), Isola (1991±97), Aula (since 1998) Petkuser Normal (1953, 1956±87), Tetra-rye (1954±55), Halo (since 1988) Strubes FruÈhe (1954±69), Kleine Schladener (1970±73), Kleine Skladia (1974±84), Diana (1985±90), Topas (1991±96), Condor (since 1997) FlaÈmingsgold (1953±70), FlaÈmingskrone (1971±85), FlaÈmingsnova (since 1986) Strubes Z (1953±65), Strubes Saturn (1966±74), Kawemono (1975±90), Victoria (1991±95), Loretta (since 1996) Dr. Delille (1953), Mandorfer (1954±62), Gelber Badischer Landmais (since 1963) Heines Heisa (1953±54), Heines Heisa II (1955±64), Breuns Wisa (1965±79), Aura (since 1980) Weiko III (1953±61, 1963±74), Sulfa (1962), Yellow III (1975±78), Refusa (1979±83), Palfa (1984±85), Topaz (1986±95), Juno (since 1996)

Oats Sugar-beet Maize Spring barley Yellow lupin a

years of cultivation.

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Fig. 3. Structure of a cereal plot. (S1±S10: row-segments to determine the yield characteristics; T1, T2: destructive sampling areas; harvest areas 1, 2, each 25 m2; & measuring point for soil moisture by using TDR-technique). Table 5 Observed yield parameters for cereals Parameter

Abbreviations

Units

Remarks

(t haÿ1)

fresh weight

Since 1953 Grain and straw yield Grain and straw yield Dry matter grain and straw Grain±straw ratio Since 1962 Germination density (plants mÿ2) Crop density (ears/panicles mÿ2) Ears/panicles per plant Kernels per ear/panicle Spikelets (fertile and sterile) Kernels per spikelet Kernel density (kernel mÿ2) Grain yield per ear/panicle Kernel weight Hectolitre weight Kernel sizes for winter rye and spring barley Kernel sizes for oats

ÿ1

YG/YS

(t ha ) (%) (1 : )

DG DC

(number) (number) (number) (number) (number) (number) (number) (g) (mg) (kg hlÿ1)

at 86% dry matter at 86%dry matter at 86% dry matter

(%) (%)

for the classes >2.8 mm; 2.8±>2.5 mm; 2.5±>2.2 mm; and 2.2 mm for the classes >2.5 mm; 2.5±>2.2 mm; 2.2±>1.8 mm; and 1.8 mm

KN DK YE/YP KW

at 86% dry matter

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Table 6 Observed meteorological parameters at Berlin-Dahlem Meteorological parameter Measurements

Unit

Measuring height automatic station Each 15 min

Measuring height climate station 7 a.m., 2 p.m., 9 p.m.

Air temperature

(8C)

shelter (2.00 m)

Maximum of air temperature Minimum of air temperature Minimum of air temperature Global radiation Net-radiation Heat flux into soil Soil temperature

(8C) (8C) (8C) (W mÿ2) (W mÿ2) (W mÿ2) (8C)

0.05, 0.20, 2.00 m, 0.20 m within the crop stand

Relative humidity

(%)

Precipitation Wind direction Wind velocity Evaporation/Evapotranspiration

(mm) (8) (m sÿ1) (mm)

Nutrient and pollutant concentration in precipitation water Soil moisture

(mg lÿ1)

2.00 m 2.00 m at soil level ÿ0.02, ÿ0.05, ÿ0.10, ÿ0.20, ÿ0.50, ÿ1.00 m ÿ0.20 m within the crop stand 0.20, 2.00 m 0.20 m within the crop stand 1.00 m 10 m 2.5 m, 10 m class-A-Pan at soil level evaporimeter after Czeratzki (1.00 m)

(Vol. %)

shelter (2.00 m) shelter (2.00 m) 0.05 m

ÿ0.01, ÿ0.02, ÿ0.05, ÿ0.10, ÿ0.20, ÿ0.30, ÿ0.40, ÿ0.50, ÿ0.65, ÿ0.80, ÿ1.00 m shelter (2.00 m) 1.00 m, at soil level evaporimeter after Mitscherlich (1.40 m) Piche (different heights) Wild (0.90 m in shelter) once a month (1.00 m) 1953±1996 once a week mainly with auger method, since 1997 with TDR-technique (0.00±0.30, 0.30±0.50, 0.50±0.80 m)

achieved which was the result of very high temperatures in July (+ 5.38C) and typical disturbances in grain set. For sugar-beet cultivation the water budget of the soil is important because beet crops can have a high water consumption, especially in warm summers. Therefore, soils with a good water capacity are better. At Dahlem the mean yield level without additional irrigation is 55 t haÿ1 (fresh weight), which is clearly above-average for a sandy soil. In 1970 and 1993, the

Among the two spring cereals, oat yield is more variable than barley (v = 28.3 and 21.0%, respectively). Oats is more sensitive to drought which could be the reason for the higher yield variability as water supply is often limiting during the growing period, especially in May and June. Variability in grain yield of maize is very high (v = 47.0%), probably due to sub-optimal temperatures and water stress in July and August. For example, in 1994 a disastrous harvest of 0.12 t haÿ1 was

Table 7 Average crop yields (t haÿ1) for the period 1953±96 (field bean 1955±96) and additional statistical parameters as Maximum highest yield, Minimum lowest yield (t haÿ1), s: Standard deviation (t haÿ1), v: Coefficient of variation % Crop

Winter ryea

Spring barleya

Oatsa

Maizea

Potatob

Sugar-beetb

Field beana

Lupina

Average Maximum Minimum s v

4.16 6.04 2.82 0.69 16.7

3.48 5.18 1.81 0.73 21.0

3.03 5.67 0.94 0.86 28.3

3.50 7.02 0.12 1.65 47.0

35.1 56.3 15.0 9.10 25.9

55.0 86.4 26.4 11.6 21.2

2.65 4.68 0.14 1.09 41.1

1.11 2.60 0.00 0.71 63.7

a b

At 86% dry matter. Fresh weight.

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F.-M. Chmielewski, W. KoÈhn / Agricultural and Forest Meteorology 96 (1999) 39±48

Fig. 4. Yields for winter rye, maize, spring barley, and oats between 1953 and 1996. The bold horizontal line shows the average yield level, the dashed vertical lines point at changes in the cultivated varieties (YG: Grain yield).

yields exceeded 80 t haÿ1 (Fig. 5). This only occurs in years with a suf®cient and well distributed water supply during the whole growth period. The level of the tuber yields of potatoes is average. Surprisingly, the variability of potato yields (v = 25.9%) is even a little bit higher than that of sugar-beet, although in literature potatoes are described as well adapted to sandy soils (Geisler, 1988). We could ®nd that yield reductions are mostly as a result of water shortage and high air temperatures during the period of tuber growth from July to August. Apparently, sugar-beet suffer less from these weather conditions than potatoes do. For example, in the hot and dry summer of 1992, potatoes showed a yield reduction of 25% compared

with the average yield while sugar-beet exceeded the average yield with about 10%. Similar results were also obtained in 1995. Yields of the grain legumes have a higher variability than those of the cereal crops. The ®eld bean shows a similar sensitivity against drought as oats, mainly during the ¯owering period in June. Later on, high temperatures and low precipitation lead to an intensi®ed ¯ower and pod abortion as well as to limited grain ®lling. The highest yield variability of all crops occurred in yellow lupin. Since 1974 the yield level has dropped rapidly and remained on a lower level (Fig. 5). The cause was a very negative reaction of the lupin to the increased number of years with extremely

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Fig. 5. Yields for potatoes, sugar-beet, field beans and yellow lupins between 1953 and 1996 (field bean 1955±96). The bold horizontal line shows the average yield level, the dashed vertical lines point at changes in the cultivated varieties (YT: Tuber yield; YB: Beet yield; YG: Grain yield).

unfavourable weather conditions for grain legumes (e.g. 1976, 1989, 1991, 1992). In other years (1982, 1984, 1994) soil-born fungi diseases were responsible for a signi®cant damage of this crop, already in the early stage of development, or for a total failure (1983, 1986, both years are omitted in Fig. 5). 5. Conclusions and planed investigations The long-term agrometeorological ®eld experiment at Berlin-Dahlem, now running for more than 45 years, is unique in Germany and probably in the world,

given the extensive dataset of meteorological and agronomic parameters. For a complex study of yield components at least 35 years (1962±96) are available. We plan to use this valuable dataset to study the relationships between atmospheric in¯uences and yield components as well as crop yields for spring cereals, and winter root crops and legumes. This knowledge can help to understand better the yield formation with respect to climatic ¯uctuations and by that the causes of the annual yield variability. Additionally, it will help to evaluate possible impacts of climatic changes on a wide range of crops which are

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grown in the north-east of Germany and comparable regions of Europe. Acknowledgements We thank all colleagues, ®rst of all Prof. G. Krzysch, C. Bergwald, H. Funke, K. Putzke and H. Recknagel who took care of the long-term ®eld experiment for many years. We are also grateful to the reviewers for providing valuable comments. References Aster, E.A.v., 1961. Die Erfassung von Witterungskonstellationen und ihrer Beziehungen zur Ertragsleistung. Z. Acker- und Pflanzenbau 112, 399±412. Chmielewski, F.-M., 1992. The impact of climate changes on the crop yields of winter rye in Halle during 1901±1980. Climate Res. 2, 23±33. Chmielewski, F.-M., Potts, J., 1995. The relationship between crop yields from an experiment in southern England and long-term climate variations. Agric. For. Meteorol. 73, 43±66. Geisler, G., 1988. Pflanzenbau, Verlag Paul Parey, Berlin und Hamburg, 530 pp. HoÈrmann, G., Chmielewski, F.-M., 1998. MoÈgliche Auswirkungen einer globalen KlimaaÈnderung auf Land-und Forstwirtschaft. In: LozaÂn, J.L., Grabl, H., Hupfer, P., Sterr, H. (Eds.), Warnsignal Klima, pp. 325±333. HuÈnicken, C., 1978. Beziehungen zwischen Witterungsverlauf und ErtraÈgen landwirtschaftlicher Nutzpflanzen sowie MoÈglichkeiten einer fruÈhzeitigen Ertragsprognose. Dissertation, Fachbereich Internat. Agrarentwicklung, Technische UniversitaÈt Berlin, 150 pp. Kleineidam, B., 1965. Der Verlauf der Bodenfeuchte unter verschiedenen PflanzenbestaÈnden in AbhaÈngigkeit von den

Witterungsfaktoren. Z. Acker- und Pflanzenbau 121, 342± 373. KoÈhn, W., 1984. Untersuchungen des Wasserverbrauchs landwirtschaftlicher Nutzpflanzen und MoÈglichkeiten seiner Berechnung mittels meteorologischer Daten. In: Krzysch, G., HuÈnicken, C., KoÈhn, W. (Eds.), Schriftenreihe Fachbereich Internat. Agrarentwicklung, Technische UniversitaÈt Berlin, Nr. IV/44, pp. 45±57. Krzysch, G., HuÈnicken, C., KoÈhn, W., 1984. Agrarmeteorologische Daten erfassungsstation zur Untersuchung des Einflusses der Witterungsfaktoren auf Entwicklung, Wachstum und Ertragsbildung landwirtschaftlicher Nutzpflanzen. Schriftenreihe Fachbereich Internat. Agrarentwicklung, Technische UniversitaÈt Berlin. Nr. IV/44, 13±29. RoÈmer, G., 1988. Die phaÈnologischen Phasen bei Hafer, Sommergerste, Winter roggen und Mais und ihre Beziehungen zur Witterung und Ertragsbildung. Dissertation, Fachbereich Internat. Agrarentwicklung, Technische UniversitaÈt Berlin, Nr. 191, 221 pp. È ber die Beziehungen zwischen WitterungsverTamm, E., 1952. U lauf und Ertragsleistung bei landwirtschaftlichen Kulturpflanzen im ehemaligen Regierungsbezirk Potsdam. Z. Acker- und. Pflanzenbau 94, 166±189. Tamm, E., Funke, H., 1955. Pflanzenklimatische Temperaturmessungen in einem Mais-Bestand. Ein Beitrag zur Beurteilung des Wachstumsfaktors Lufttemperatur. Z. Acker- und. Pflanzenbau 100, 199±210. È ber die Ausbildung Tamm, E., Graetz, H., Funke, H., 1956. U pflanzenklimatischer Temperaturen in BestaÈnden landwirtschaftlicher Nutzpflanzen. Z. Acker- und Pflanzenbau 101, 193±232. Tamm, E., Krzysch, G., Funke, H., 1959. Untersuchungen uÈber die Ausbildung des Mikroklimas in landwirtschaftlichen NutzpflanzenbestaÈnden. Z. Acker- und Pflanzenbau 109, 355± 383. Tamm, E., Krzysch, G., Funke, H., 1965. Aufbau und Messtechnik der Pflanzen-Wetterstation in Berlin-Dahlem. Z. Acker- und Pflanzenbau 122, 334±358. Walter, H., Lieth, H., 1960. Klimadiagramm-Weltatlas, Fischer Verlag, Jena.