Farming in Response
to the Weather:
A Guide for Extension
Sue Walker
Agrometeorology Professor, University of the Free StateDept 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
• Water essential for growth & development• If low water available give low production
• Climate variability high in semi-arid tropics
Gl e n Longt e r m Annua l Ra i nf a l l
547
0
200
400
600
800
1000
1200
1920 1930 1940 1950 1960 1970 1980 1990 2000Time
Rai
nfal
l (m
m)
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?• e.g. hay making, irrigation, planting, spraying,
� What information can be provided to reduce risk or to assist in planning these decisions?• 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
Use participatory needs assessmentVenda village meeting
For farmers’ study groups
Each bring information:� Farmers
• aim of farming • household food or for
market
• local knowledge• availability of resources
• stored seed
• manure / mulch
• Labour
� Extension • seed & inputs availability• market location• communication skills
� Agromet• long-term trends• Current season outlook• Monitor current season rain
Examples from farmers :
a) Resource status informationb) On-farm decision-making
i. Planting calendarii. Crops problem areasiii. Management options
� 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
0
100
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300
400
500
600
700
800
900
110 120 130 140 150 160 170 180 190 200 210
Number of day(1-365) when season starts
Tot
al s
easo
nal r
ainf
all (
mm
)
Niamey, 1954-83
Agromet discussion materials
ii. Typical yield production function
02468
101214
0 1 2 3 4
Water Use
Tot
al D
ry M
atte
r
Agromet discussion materials
iii. Simulated yield from crop models
0 300 600 900 1200 1500 1800 2100 2400 2700 3000 3300 36000
0.2
0.4
0.6
0.8
1
Grain yield (kg/ha/year)
Pro
babi
lity
of n
on-e
xcee
danc
e
Full
3/4
1/2
1/4
Empty
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 siteCumulative Distribution Function of Rainfall
0
0.33
0.66
0.99
0 200 400 600 800 1000 1200 1400 1600
Rainfall (mm)
Pro
babi
lity
of n
on-e
xcee
danc
e
Pietermaritzburg
Bethlehem
Bloemfontein
Upington
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?
� What area to plant?
� What plant density?
� What inputs?
Agromet model options
� Maize / sorghum / sunflower / beans
� Deep / shallow soil� Hi / lo potential soil
� Hi / medium / lo
� Manure / mulch / pest control
Example - tillage optionsConventional full tillage versus
in-field water harvesting
runoff areaCollection
& infiltration
in-field water harvesting on clay soil Conventional
Water harvesting
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
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000 6000
Yield (kg/ha)
Cum
ulat
ive
prob
abilit
y
EmptyHalfFull
Conventional tillage
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000 6000
Yield (kg/ha)
Cum
ulat
ive
prob
abilit
y
EmptyHalfFull
Water harvesting
For example, probability of 50% (i.e. half years) ofproducing 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
Different Cultivars (time to maturity)
0.00
0.25
0.50
0.75
1.00
0 2000 4000 6000
Yield (kg/ha)
Cum
ulat
ive
prob
abilit
y
EarlyMediumLate
Conventional tillage
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000
EarlyMediumLate
Water harvesting
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
short
long
short
long
Different Plant Densities
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000 6000
Yield (kg/ha)
Cum
ulat
ive
prob
abilit
y
LowOptimumHigh
Conventional tillage
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000 6000
LowOptimumHigh
Water harvesting
For example, probability of 50% producingless than 1.80, 2.39 and 2.30 t ha-1 for CT andless than 2.01, 3.77 and 4.64 t ha-1 for WHwith low, optimum and high plant densities
Different Planting Dates
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000 6000
Yield (kg/ha)
Cum
ulat
ive
prob
abilit
y
NovDecJan
Conventional tillage
0.00
0.25
0.50
0.75
1.00
0 1000 2000 3000 4000 5000 6000
Yield (kg/ha)
Cum
ulat
ive
prob
abilit
y
NovDecJan
Water harvesting
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 WHwith November, December and January sowing dates
Develop decision tables
farmeragromet
extension
Pre-season cropping decisions
Simulated yield a/c to management
Potential Crop Yield
Examples of options
Monitoring weather data
TransportLabourrequired
Monitoring weather data
Labouravailable
3-6month seasonal outlook
Commod-ity prices
Land preparation
Daily forecasts of rain & temp.
Post-harvest storage
Fair, dry weather to harvest
Late season
Weekly /dekadal forecast of rain & temperature
Availability of inputs
Cash for inputs
Mid-season
Long-term means & probabilities
MarketsSeed available
Pre-planting
AgrometExtensionFarmerTime in season
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 Farming2. From ‘data’ and ‘knowledge’ to ‘information’3. Towards Use of Decision-making Tools
Lets help the farmers make a success
under variable weather conditions