International Summer School in agrometeorology and crop modelling

18 Pages·2016·18.96 MB·English

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International Summer School in
agrometeorology and crop modelling

Book of lectures and exercises
Introduction
Challenges in agrometeorological research and application
Agrometeorology: from fundamental to applied
Agrometeorological models, fundamentals

Topic 1 – Agrometeorology
Crop growth factors: radiation, energy balance, and temperature
Crop growth factors: water and water balance
Impact of weather extremes on crop growth and adaptation options
Measuring microclimates: challenges and applications
Physics of the boundary layer

Topic 2 – Numeric Weather Prediction
Parameterization of processes in soil-vegetation-atmosphere transfer schemes:
water and energy balance
Building SVAT models: epistemiological features
Building SVAT models: an example with the LAPS scheme
Building SVAT models: links with other models

Topic 3 – AquaCrop
AquaCrop model: fundamentals and database management
AquaCrop manuals
UNIFI
Training:
• Crop modeling (theory and training)
H2020-TWINN-2015 •• E Acgoropmhyestieoolorgloicgaicl aml oinnditeoxr mingo dels
• From theory to prac;ce in agrometeorology
• Project development and management
SERBian-Austrian-Italian (SAI)
BOKU PFNS
partnership FORcing EXCELLence
in ecosystem research
Welcome
AgMnet INTERNATIONAL
SUMMER SCHOOL
IN AGROMETEOROLOGY T• Aragirnoimnge:t eorological measurements and
AND CROP MODELLING data analysis
• Agrometeorlogicalindex models and
27 June – 01 July 2016 i•m Frpoamct t ahlegoorryit thom psr ac;ce in
Novi Sad, SERBIA agrometeorology
• Project development and management
Summer School, Novi Sad,O JPuEnNeN 2I0N1G6 Summer School, Novi SadO, PJEunNeN 2I0N1G6
PROJECT PLAN

“... is just a wishful thinking if goals are not achievable"
Plant abiotic stress
C tolerance
P I Crop management
O methods to reduce
T evapotranspiration
H Spatial distribution of
C precipitation
R
A Atmosphere-soil-plant
E turbulent transfer
S
R E S caAospnwspdeelisaitcistohainnteisgro npfeo rnorev fda inripcouptnmieomaenrer aiinncnta calel ArmIetnailay,, RAeupsturbiali,c Boofs Mniaac aenddon Hiae raznedg oTvuirnkae,y B. u l garia, Croatia, Czech Republic, Hungary,
of harmful organisms
INTRODUCTION
Summer School, Novi Sad, June 2016
I wish you hard work, luck of sleep,
endless discussions, new ideas, good
energy & beautiful weather ...

EVERYTHING ELSE WILL COME BY
ITS OWN!
Summer School 2016

Agrometeorology
Novi Sad, June 27 - July 1 2016
plays an increasing important role
in agriculture and food production !

Why?
Global change
leads to
higher risks in agricultural production
Josef Eitzinger and less resources for more people.
Institut für Meteorologie, Universität für Bodenkultur, Wien
E-mail: [email protected]
http://www.boku.ac.at/
With special thanks to Federica Rossi (Italy) and Branka Lalic (Serbia) for providing some slides
6
Global crop suitability
Why family (traditional) farms are so important
for global food security and welfare of countries ?

Family farms produce about 80 percent of the world's food.

84% are smaller than two hectares.

Farms above 50 hectares occupy two-thirds of global farm land.

In low-income countries, farms smaller than five hectares
occupy up to 70% of farm land.
2005 vs 2055
In most developing countries farms are becoming
smaller and smaller.
FAO, 2014 Lane and Jarvis, 2007
Climate change impacts on crop yields in Africa 2030 vs. 2000
Challenges for
(20 GCMs ensemble mean)
operational agrometeorological application
and future research

• Monitoring activities:
Real time and forecasts (drought, extreme weather etc.)

• Decision Support Systems:
Application and user oriented, economic, short and long term focus

• Climate Mapping:
High spatial resolution, considering climate change and crop specific aspects

• Improving and combining the tools:
Remote Sensing, GIS, agrometeorological , crop and irrigation models,
measurement systems, data transfer and processing etc.

(Lobell et al., 2008. Science)
Improving, optimising, ....
e,... Crop yields,
c
n
a food production
port, insur methods Food quality ..... by inucrsee aosfi ningp tuhtes efficient
al sup y and (fertilin(zsaeotriu,l ,rm waala crtheesirno, eucrrryoc,pe .s,. .) and
on og Farmers income microclimate)
stituti chnol
n e
e, i m t Landscape functions,
ur ar biodervisity But is that
uct F always sustainable ?
astr Risk
nfr of production ...
I
Forecasting weather …
meteorological Service of Serbia NWP products of interest in
Republic HydroSource: ••d SMahyeosdr)i tu-rman-rgaen wgee awtehaetrh feoarr efgocrraeosctm a(soteu (tto etuoot 5tro do 1al5yo s)g y
• Monthly forecast (10 to 30 days)
• Seasonal forecast (out to 7 months)
• Climate model simulations (decades)
Lalic, EMS11, September 2011, Berlin, Germany
On site measurements are crucial for
many agrometeorological applications
(e.g. crop protection)
Abb. 3.6
Drought monitoring

(Source: Susnik, Drought Management Center for South eastern Europe
(DMCSEE); www.dmcsee.org)
ADA Web-Interface
Importance of ground truth data:
Transect measurements for high resolution
Combination of spatial climate mapping
spatial data bases:
High resolution
water balance
ETgages:
placed in 20m
and 80 m
distance from
the hedgerow
Example: Surface Soil Moisture ASCAT Scatterometer 500m (lee side)
Vs. Soil map
Wineyard conditions (climatic terroir): Remote Sensing Methods:
Daily air humidity in June at 0.5 m (mean 1990-2009)
Improving knowledge on spatial variabilites of surface
conditions

Below: Spatial soil varibilities (Hymap, Marchfeld)
Precision farming: Spatial yield distribution, winter wheat 20 Ohne Zunahme von Extremereignissen !
wirkung auf Winterweizenertrag (%)-11000 2020er ohne C20O520e er ffekt 2080er 2020er mit C2O052 0eefrfeEHCkCGatdHCCAMMM1224080er
Aus CGSFIDRLO-R-M15k2b
-20 Modellierte Klimaszenarien
2020s 2050s 2080s
a) 0 1 2 3 Crop model applications
-10
N (days)-20 ECHAM4 Climate cha nge impacts
(cid:39) HadCM2
-30 CGCM1 on winter wheat yields
CSIRO-Mk2b
-40 GFDL-R15 in Austria
(Alexandrov and Eitzinger, 2001)
Schaupenlehner et al., 2008
Increase of water stress
(simulated for spring barley - eastern Austria
Spring wheat yield change (%)
2050
between minimum and
conventional tillage for baseline
(1961-1990)
and climate change scenario
(2041-2050 HADCM3-A2)
(Simota, 2009)
(Thaler et al., 2008)
Spatial scale: 1:25000 digital soil map – 5 soil classes
Difference between date of the last frost with
the return probability 2 and 20 years
RCM - Frost risk (Agriclim) RCM - Sowing conditons (early spring)
Median = 14 days Median = 16 days Median = 16% Median = 16%
Min = 6 days Min = 7 days Min = 0% Min = 0%
Max = 38 days Max = 33 days Max = 53% Max = 60%
31 32
Potential deviations between crop models – simulated yield
Crop model applications – understanding uncertainties is crucial !
INTRODUCTION
Summer School, Novi Sad, June 2016
H2020-TWINN-2015 "Be very, very careful what you put into that head,
because you will never, ever get it out".

Thomas Cardinal Wolsey (1471-1530)
Agrometeorology between Agrometeorology is an interdisciplinary holistic science
which is a real bridge between physical and biological
applied and fundamental sciences
Effects of CC, EWE, chronic undernourishment (805 million
in the world 2012-2014 (FAO, 2014)) ...
Branislava Lalić
Agrometeorology - inevitable element of the solution
AgMnet INTERNATIONAL
SUMMER SCHOOL
IN AGROMETEOROLOGY
AND CROP MODELLING Faculty of Agriculture, University of Novi Sad, Serbia

27 June – 01 July 2016
Novi Sad, SERBIA
23.-24. May 2016 7th CASEE Conference, Temisoara (Romenia) 23.-24. May 2016 7th CASEE Conference, Temisoara (Romenia)
OBSERVED CHANGES IN PHENOLOGY DYNAMICS
Region Dates Change (days)
13.03.2015.
Novi Sad - 58
14.01.2016.
B a č k a 19.03.2015.
- 58
Topola 20.01.2016.
12.03.2015.
Pančevo - 26
15.02.2016.
GROWING PROBLEM
22.02.2015.
Ruma - 53
31.12.2015.* Shift in appearance of "four tillers
03.03.2015. detectable" - growing stage of winter wheat
Sombor 21.12.2016. - 73 in Serbia (Source: PIS Serbia).
*
23.-24. May 2016 7th CASEE Conference, Temisoara (Romenia) 23.-24. May 2016 7th CASEE Conference, Temisoara (Romenia)