It is undeniable that the global warming has already affected the Earth’s biota, whereby the rise of air temperature is an important factor. Agricultural systems are also affected by climate change via the interrelated biophysical layers. Climate influences farmers` decisions in crop management. To simulate the interactions between climate/weather and the different crop management activities an agent based modelling approach is used, in which farmers` decision making is based on crop requirements from literature. To validate these decision algorithms on how farmers arrange their daily crop management decisions like planting, fertilizing, and harvesting due to changes of climate parameters, a statistical analysis of empirical data (1970-2003) on temperature and different crop growth stages, which represent different management activities, was carried out. Results show that every crop has to be considered separately and the requirements of the different crops on temperature have to be observed in different ways. There are crops which have a low germination temperature, for those the average daily temperature shows no relation with the planting day. In this case the temperature sum in a specific period is more precise. On the opposite side crops with a high germination temperature show significant correlation results with the average daily temperature of a period before the planting day.Crop management, heuristic decisions, regional climate change, Crop Production/Industries, Farm Management,

Apfelbeck, Josef

Huigen, Marco

Krimly, Tatjana

Sanchez, G.C.

English

Research Papers in Economics

  1 
12
th Congress of the European Association of Agricultural Economists – EAAE 2008 
MANAGEMENT DECISIONS ON FARM-LEVEL AND THEIR LINK TO WEATHER 
REQUIREMENTS: A CASE STUDY FOR THE UPPER DANUBE RIVER BASIN 
Apfelbeck J. 
1, Huigen M. 
1, Krimly T.
1, and Sanchez G.C. 
1 
1 Universität Hohenheim/Institute for Farm Management, Schloss Osthof Süd, Stuttgart, Germany  
 
Abstract  — It is undeniable that the global warming has 
already affected the Earth’s biota, whereby the rise of air 
temperature is an important factor. Agricultural systems are 
also affected by climate change via the interrelated bio-
physical layers. Climate influences farmers` decisions in crop 
management. To simulate the interactions between 
climate/weather and the different crop management activities 
an agent based modelling approach is used, in which farmers` 
decision making is based on crop requirements from literature. 
To validate these decision algorithms on how farmers arrange 
their daily crop management decisions like planting, 
fertilizing, and harvesting due to changes of climate 
parameters, a statistical analysis of empirical data (1970-2003) 
on temperature and different crop growth stages, which 
represent different management activities, was carried out. 
Results show that every crop has to be considered separately 
and the requirements of the different crops on temperature 
have to be observed in different ways. There are crops which 
have a low germination temperature, for those the average 
daily temperature shows no relation with the planting day. In 
this case the temperature sum in a specific period is more 
precise. On the opposite side crops with a high germination 
temperature show significant correlation results with the 
average daily temperature of a period before the planting day. 
 
Keywords  — Crop management, heuristic decisions, 
regional climate change 
I.  INTRODUCTION  
In crop production the choice of location for a 
specific crop is influenced by the three main factors 
climate, soil and the requirements of the crop. The 
yield is determined by these three factors. Another 
important aspect of productivity is the crop rotation 
and the cultivation way of the farm manager. The 
often discussed climate change leads to a change of 
these local and natural factors. The natural 
environment indicates this by the earlier appearance of 
phenological stages [1]. Although the climatic 
conditions of a location are slowly changing, the 
natural environment is a main factor in rating the 
economical efficiency of a location [2]. To understand 
this slowly changing interaction between 
climate/weather and the crop production decisions of a 
farmer, which are often based on economical aspects, 
an agent based model was build up. This model was 
calibrated on the catchment’s area of the river Danube. 
The model simulates the daily decisions of a farmer 
under different climatic conditions. The main purpose 
lies on the survey of changing planting, fertilizing, and 
harvesting dates and the resulting changes on yield. 
These decisions are influenced by the knowledge of 
farmers on the past years and the actual weather. The 
decision algorithm is based on the different 
requirements of the different crops and stages, 
whereby the main purpose lies on the decision of 
planting the crop. With the date of planting the 
growing period of the plant is integrated in the local 
weather course. The necessary information about 
development stages and yield will be provided by a 
crop growth model. To emphasize the parameters of 
the decision algorithm a statistical analysis on 
empirical data concerning the date of planting and the 
temperature was carried out. 
II.  METHOD 
The project area is the Upper Danube catchment 
which covers an area of 77,000 km² and it includes 
glaciers as well as temperate lowlands, which are 
intensively used by agriculture.  
The agent-based model is based on so-called 
heuristic agents. These are agents which have relative 
simple rules that guide them in their decision-making. 
In general the underlying decision trees on planting, 
fertilizing, and harvesting were realized by creating 
deductive decisions out of general knowledge on the 
climate requirements of different crops mainly based   2 
12
th Congress of the European Association of Agricultural Economists – EAAE 2008 
on literature. Figure 1 shows the decision to plant 
winter wheat, which depends on the crop requirements 
in specific stages, like e.g. the need of enough rainfall 
during heading and the actual weather condition at the 
certain day the agent planned to plant. 
 
 
Figure 1: Decision-tree for planting winter wheat 
 
These decision trees shall be improved by 
combining it with empirical data to strengthen the 
decisions with inductive facts. To proof the 
interrelation between weather parameters and 
management activities, data from the DWD 
(Deutscher Wetter Dienst) on the daily average 
temperature and observed development stages of 
different crops for the period from 1970 to 2003 was 
used. To find a connection between climate 
parameters and these requirements the data on the 
different districts was separated in the following two 
groups: 
First of all the total catchment area was considered 
as homogeneous, there is no difference between 
specific districts, climatic zones and elevation. 
Secondly for every district it was taken the average 
value over the period of the thirty years per district. In 
this case the districts can be compared with each other. 
The yearly variability was not considered anymore. 
This differentiation was taken into account to get a 
sense about parameters which are hidden in the dataset 
and are considered as constant, because in the 
correlation only one parameter the daily average 
temperature is considered. The advantage is, that later 
in the result set a correlation value near one shows the 
secondary role of other parameters which were not 
considered in the correlation. 
These two groups were analysed in three different 
ways: 
•  In the first approach several periods of time 
before the planting day (60, 40, 30, 10, 5 
and 3 days) were taken for each crop and 
the average temperature from each 
respective period was correlated with the 
planting day.  
•  The second approach was based on 
literature review on phenological stages of 
natural vegetation where plants and their 
growing stages were predicted by a specific 
temperature sum above a specific basis 
temperature [3]. For this observation, the 
days with daily average temperature >=5°C 
from the first of January until the end of 
February, March, April, May, and June 
were taken. Then the planting day was 
correlated with the temperature sum lasting 
from the first of January until the end of the 
defined months. 
•  In the third approach the vegetative start 
and end were calculated. The value is 
computed with the average daily air 
temperature. The vegetative start is the first 
day in the year, which endures the 
temperature of 5°C. This is quantified by 
the following thirty days, which have to 
fulfil the following term: 
i
i
(T  - 5°C) > 0°C (i= 2, 3...30) ∑ (1).  The 
vegetative end is defined as the day where 
the value is lower than 5°C and the 
following days fulfil the following 
condition: 
i
i
(T  - 5°C) < 0°C (i = 2, 3 ...end of year) ∑
(2) [4].   3 
12
th Congress of the European Association of Agricultural Economists – EAAE 2008 
This date of the vegetative start or end was 
correlated with the planting day of the specific crops. 
III.  RESULTS 
Results show that every crop has to be considered 
separately and the requirements of the different crops 
on temperature have to be observed in different ways. 
There are crops which have a low germination 
temperature, for those the average daily temperature 
shows no relation with the planting day. In this case 
the temperature sum in a specific period is more 
precise.  
Sugar beet showed an increasing correlation value 
with the temperature sum from March until May, 
where the correlation reached a value of -0.675 at the 
0.05 level. The conclusion is that the higher the 
temperature sum the earlier sugar beet is planted in 
spring. This is in line with the strategies of farmers to 
increase the yields by lengthening the growing period 
of sugar beets. Similar results were found by 
correlating spring barley and oat with the temperature 
sum of specific months. The temperature sum until the 
end of March showed for both highly significant 
correlation values, -0.586 for oat and -0.729 for spring 
barley. 
On the other hand crops with a high germination 
temperature show significant correlation results with 
the average daily temperature of a period before the 
planting day. For example maize needs a relatively 
high temperature about 8-10°C [5]. The correlation 
results showed that there is a correlation at the highest 
level (0.01) within 10 days before planting, where the 
temperature was around 10.5°C.  
In the third approach the different crops were 
correlated with the vegetative start or end. In spring 
the planting of oat and spring barley starts with the 
start of the vegetative growth. The correlation values 
of oat, spring barley, and sugar beet are very close 
together, in a range between 0.505 and 0.653 on the 
significance level of 0.05. 
IV. DISCUSSION & CONCLUSION 
The involvement of inductive data is an adequate 
possibility to improve the heuristic management 
decisions, based on literature, for the specific 
development stages of the plants. The correlation 
results reflect this issue by showing up the connection 
between the days of planting and the required 
temperature levels of the different seeds. However, 
crop management activities are not only influenced by 
temperature, although this is an integral part of a plant 
growing period. The farmer has to consider also other 
parameters like precipitation or day length. In a next 
step these parameters shall be integrated into the 
observation, which results in a regression analysis. At 
the end the regression analysis shall show up risk 
factors caused by climatic parameters, which might 
improve the crop management decisions in the agent 
based model. 
REFERENCES 
1.  Schröder W, Schmidt G, Hasenclever J, (2006) 
Geostatiscal analysis of data on air temperature and 
plant phenology from Baden-Württemberg (Germany) 
as a basis for regional scaled models of climate change. 
Environmental Monitoring and Assessment 120: 27-43. 
2.  Bahrs E, Rust I, (2004) Notwendigkeit und 
Konsequenzen einer aktualisierten Bodenschätzung in 
der Landwirtschaft aus betriebswirtschaftlicher Sicht, 
In: Perspektiven in der Landnutzung – Regionen, 
Landschaften, Betriebe – Entscheidungsträger und 
Instrumente. Band 39 der 43. Gewisola-Tagung in 
Hohenheim: 461-470. 
3.  Chmielewski F-M, Müller A, Küchler W, (2004) 
Mögliche Auswirkungen klimatischer Veränderungen 
auf die Vegetationsentwicklung in Sachsen. In: 
Abschlussbericht zum Forschungsprojekt; Eigenverlag 
Humboldt-Universität Berlin. 
4.  Chmielewski F-M, Foken Th, (2003) Rezente 
Veränderungen der Lufttemperatur und der 
Niederschlagshöhe in Berlin-Dahlem, 1931-2000. In: 
Beiträge zur Klima- und Meeresforschung, Berlin und 
Bayreuth: 79-90. 
5.  Diepenbrock W, Fischbeck G, Heyland K-U, Knauer N, 
(1999) Spezieller Pflanzenbau. 3. Auflage. Verlag 
Eugen Ulmer, Stuttgart. 
 
•  Author: Josef  Apfelbeck 
•  Institute:  Universität Hohenheim,  
Institute for Farm Management 
•  Street:    Schloß Osthof Süd 
•  City:   70593  Stuttgart 
•  Country: Germany 
•  Email: apfelb@uni-hohenheim.de 
 

Geostatiscal analysis of data on air temperature and plant phenology from Baden-Württemberg (Germany) as a basis for regional scaled models of climate change.

Mögliche Auswirkungen klimatischer Veränderungen auf die Vegetationsentwicklung in Sachsen. In: Abschlussbericht zum Forschungsprojekt; Eigenverlag Humboldt-Universität

Notwendigkeit und Konsequenzen einer aktualisierten Bodenschätzung in der Landwirtschaft aus betriebswirtschaftlicher Sicht, In:

MANAGEMENT DECISIONS ON FARM-LEVEL AND THEIR LINK TO WEATHER REQUIREMENTS: A CASE STUDY FOR THE UPPER DANUBE RIVER BASIN

MANAGEMENT DECISIONS ON FARM-LEVEL AND THEIR LINK TO WEATHER REQUIREMENTS: A CASE STUDY FOR THE UPPER DANUBE RIVER BASIN

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