Farming in Response
to the Weather:
A Guide for Extension
Sue Walker
Agrometeorology Professor, University of the Free State
Dept Soil, Crop and Climate Sciences,
Bloemfontein, South Africa
Introduction
Weather parameters affect crop production
•
Temperature affects plant growth
• Very high or very low give less growth according to crop
species
•
1200
• If low water available give low production
•
Climate variability high in semi-arid tropics
(m m )
1000
Water essential for growth & development
800
R a in fa ll
600
547
400
200
0
1920
Gl e n Longt e r m Annua l Ra i nf a l l
1930
1940
1950
Time 1960
1970
1980
1990
2000
Introduction cont.
Farmers’ decisions
•
•
•
When to plant?
What crop to plant?
Where & how much to plant?
Use local indigenous
knowledge
Introduction cont.
Need to answer following:
Which on-farm routine operations are dependent
on the weather?
•
What information can be provided to reduce risk or
to assist in planning these decisions?
•
•
•
e.g. hay making, irrigation, planting, spraying,
daily weather forecasts
weekly forecasts
seasonal outlooks
Can one farm in response to weather?
Original “Response Farming” Concept
Developed by Dr. J. Ian Stewart in 1980s
WHARF (World Hunger Alleviation through Response Farming)
Use interaction of rainfall and farming system
to optimize crop production
Need following:
• start date and amount of rainfall
• yield for corresponding amount of rain
• use to construct a “rainfall flag”
“rainfall flag”
graph
Y-axis is rainfall (mm)
X-axis is yield and rain
onset date
Example Davis CA from Stewart, 1988.
To show probabilities:
•
•
Y-axis is rainfall
X-axis is specific groups of
ranges of onset dates
Example Davis CA from Stewart, 1988
Assumptions for response farming
Assume
•
•
•
Early onset of rain means more rain will be received
Onset date is proportional to amount
Onset is related to production
Can only be true if ‘end’ of rain season is
stable each year but not strictly true
everywhere
Need to modify some definitions
Expand response farming concept
From
data and knowledge
to useful information
From data
Use data
•
•
•
•
Climate - need long-term daily rainfall
Soil – depth & type & water holding capacity
Crop – type & agronomic management variations
Socio-economic – farmers’ aims & markets etc
Via calculation & analysis & manipulation
To useful information
•
•
•
Rain onset date & seasonal amount
Potential crop yield for certain rainfall
Information applied according to farmers’ needs
From indigenous knowledge
All farmers have valuable information as inputs
Local farmer information about practical
farming systems
•
•
•
•
•
Local seeds & landraces characteristics & availability
Location of shallow &/ poor soil
Microclimate variations (e.g. wind, temperature variation)
Pests & diseases occurrences
etc.
Integrate into
information sources
All role-players have valuable inputs
Agrometeorologist - climate data analysis
Extension - experience in area
Local farmer - information about practical
farming systems
Venda village meeting
Use participatory needs assessment
For farmers’ study groups
Each bring information:
Farmers
•
household food or for
market
local knowledge
availability of resources
•
•
•
stored seed
manure / mulch
Labour
Extension
•
•
•
aim of farming
•
•
•
seed & inputs availability
market location
communication skills
Agromet
•
•
•
long-term trends
Current season outlook
Monitor current season rain
Examples from farmers :
Resource status information
On-farm decision-making
a)
b)
Planting calendar
ii. Crops problem areas
iii. Management options
i.
Collect data using participatory
methods
a) Resource status information
Farmers access
resources
soil types
access to water
transport
pests
co-op
markets
Map of Hoxane
Irrigation Scheme
b) On-farm decision-making –
i. Timing of field operations
soil tillage / preparation / planting / weeding
Time line
of maize production
at Veerplaats
b) On-farm decision-making –
ii. According to current weather
Which are most dependent on weather?
• e.g.
• frost can destroy young sensitive plants
• high temperature causes heat stress & wilting
• rain soften soil crust for seedling emergence
• heat stress reduce milk produced
• vegetables need frequent rains
• etc.
b) On-farm decision-making –
iii.
Ranking of problems encountered
Matrix ranking allow each
farmer to
vote for
problems
encountered
with various
crops
crops
b) On-farm decision-making –
iv.
Farmers decision options
Below normal rainfall:
•
•
•
•
•
•
•
Plant animal fodder crops
Less maize
More sorghum
Lower density
Plan to try adding water
Sell animals
Take animals to grazing
Above normal rain: (good rains)
•
•
•
•
•
•
plant earlier
Grow more vegetables
Grow more cash crops
Increase sharecropping
Watch for pests & diseases
(crops & stock)
Winter breeding for sheep &
goats
From participatory survey in Lesotho
by Dr G Ziervogel
Steps for Agromet calculations
Compile dataset
• Daily rainfall amount
• Crop yield
Analyze data
• Onset of rain
• Length of season
• Seasonal total rainfall
Prepare discussion materials
Agromet discussion materials
i.
Seasonal rainfall versus onset dates
900
Niamey, 1954-83
Total seasonal rainfall (mm)
800
700
600
500
400
300
200
100
0
110
120
130
140
150
160
170
180
Number of day(1-365) when season starts
190
200
210
ii. Typical yield
production function
Total Dry Matter
Agromet discussion materials
14
12
10
8
6
4
2
0
0
1
2
3
Water Use
4
Agromet discussion materials
iii. Simulated
yield from crop
models
Probability of non-exceedance
1
0.8
Full
3/4
0.6
1/2
1/4
0.4
Empty
0.2
0
0
300
600
900 1200 1500 1800 2100 2400 2700 3000 3300 3600
Grain yield (kg/ha/year)
Long term maize yields using the Putu crop model
under conventional tillage on Glen/Bonheim ecotope,
starting with 5 initial soil water content regimes
planted in mid-December
Climate data, with effective rainfall,
from Glen College, 1922-2001.
Agromet discussion materials
iv. Rainfall probabilities for each site
Cumulative Distribution Function of Rainfall
Probability of non-exceedance
0.99
0.66
0.33
Pietermaritzburg
Bethlehem
Bloemfontein
Upington
0
0
200
400
600
800
Rainfall (mm)
1000
1200
1400
1600
Agromet
discussion materials
v. Seasonal rainfall
forecasts for region
Develop decision tables
“What if” discussion with all parties concerned
Long-term graphs
3-month seasonal rainfall outlook
Current rainfall situation
Discuss local available options and outcomes
Integrate model & local information
Local options for decision tables
Farmers questions
Which crop?
Agromet model options
Maize / sorghum / sunflower / beans
What area to plant? Deep / shallow soil
Hi / lo potential soil
What plant density? Hi / medium / lo
What inputs?
Manure / mulch / pest control
Example - tillage options
Conventional full tillage versus
in-field water harvesting
in-field water harvesting on clay soil
Conventional
Water harvesting
runoff area
Collection
& infiltration
Example with tillage options
• Compare simulated maize yields for
conventional tillage (CT) and
in-field water harvesting (WH)
for range of farmer options:
• When to plant? Nov / Dec / Jan
• How much seed to use? Low / medium / high plant populations
• What cultivar to use? short / medium / long growth period
• How much water to start? empty / half / full soil profile
Different Initial Soil Water
1.00
1.00
Water
harvesting
0.75
Cumulative probability
Cumulative probability
Conventional
tillage
0.50
0.25
Empty
Half
Full
0.00
0.75
0.50
0.25
Empty
Half
Full
0.00
0
1000
2000
3000
4000
Yield (kg/ha)
5000
6000
0
1000
2000
3000
4000
Yield (kg/ha)
For example, probability of 50% (i.e. half years) of
producing less than 1.38, 2.23 and 2.90 t ha-1 for CT
and less than 3.27, 3.52 and 3.63 t ha-1 for WH
with empty, half and full initial soil water, respectively
5000
6000
Different Cultivars
1.00
1.00
Water
harvesting
Conventional
tillage
Cumulative probability
(time to maturity)
0.75
0.75
0.50
0.50
Early
Medium
long
Late
0.25
Early
short
Medium
Late
0.25
short
long
0.00
0
0.00
0
2000
4000
1000
2000
3000
4000
6000
Yield (kg/ha)
For example, probability of 50%
producing less than 2.18, 2.17 and 2.15 t ha-1 for CT
and less than 3.58, 3.50 and 3.34 t ha-1 for WH
with cultivars of short, medium and long time to maturity
5000
Different Plant Densities
1.00
1.00
Water
harvesting
Cumulative probability
Conventional
tillage
0.75
0.75
0.50
0.50
0.25
Low
Optimum
High
Low
Optimum
High
0.25
0.00
0.00
0
1000
2000
3000
4000
5000
6000
0
1000
2000
3000
4000
Yield (kg/ha)
For example, probability of 50% producing
less than 1.80, 2.39 and 2.30 t ha-1 for CT and
less than 2.01, 3.77 and 4.64 t ha-1 for WH
with low, optimum and high plant densities
5000
6000
Different Planting Dates
1.00
1.00
Water
harvesting
Cumulative probability
Cumulative probability
Conventional
tillage
0.75
0.50
0.25
0.75
0.50
0.25
Nov
Dec
Jan
Nov
Dec
Jan
0.00
0.00
0
1000
2000
3000
4000
Yield (kg/ha)
5000
6000
0
1000
2000
3000
4000
Yield (kg/ha)
For example, probability of 50% of
producing less than 2.22, 2.49 and 1.80 t ha-1 for CT and
less than 3.97, 4.00 and 2.45 t ha-1 for WH
with November, December and January sowing dates
5000
6000
Develop decision tables
agromet
farmer
extension
Pre-season cropping decisions
Simulated yield a/c to management
Potential Crop Yield
Examples of options
Time in
season
Farmer
Extension
Preplanting
Seed
available
Land
preparation
Markets
Mid-season
Cash for
inputs
Labour
available
Availability
of inputs
Late
season
Fair, dry
weather to
harvest
Labour
required
Post-harvest
storage
Agromet
Commod Long-term
-ity prices means &
probabilities
3-6month
seasonal
outlook
Weekly
Monitoring
/dekadal
weather
forecast of rain data
& temperature
Transport
Daily forecasts
of rain &
temp.
Monitoring
weather
data
Conclusions
Should be study group with farmers, extension
& agrometeorologist
Use local knowledge & model outputs to
simulate potential variation according to
management practices
Farming more viable if done in response to
long-term climate info and seasonal forecast
together with current weather information
Publication
“Farming in Response to the Weather:
A Guide for Extension”
by S Walker and H Pfeiffer
Chapters to include:
1. Stepwise Data Analysis for Response Farming
2. From ‘data’ and ‘knowledge’ to ‘information’
3. Towards Use of Decision-making Tools
Lets help the farmers
make a success
under variable weather conditions