Weather based precision farming in semi Weather based precision - - PowerPoint PPT Presentation

Weather based precision farming in semi Weather based precision farming in semi- arid tropics arid tropics

• Dr. D. Raji Reddy

Principal Scientist (Agromet.) dandareddy009@gmail.com and

• Dr. G.Sreenivas,

Senior Scientist (Agronomy) gsreenivas2002@gmail.com Agromet-Cell, Agricultural Research Institute, ANGR Agricultural University, Rajendranagar, Hyderabad

I di ’ A il L d d F d d ti India’s Agril. Land and Food production

Rainfed agriculture( 6 0 % ) Rainfed agriculture( 6 0 % ) (45% food production)

(85 Mha) (57 Mha) (85 Mha) (57 Mha)

I rrigated agriculture ( 4 0 % )

( 5 5 % food production)

Weather/Climate information will be useful in Agriculture Weather/Climate information will be useful in Agriculture

• Procurement of inputs for timely sowing

p y g

• To plan cropping systems
• Selection of crop/variety
• Selection of crop/variety
• Timely sowing/transplanting
• Irrigation scheduling
• Fertilizers application
• Timing of plant protection & reduce indiscriminate

pesticide usage

• Harvesting
• Marketing

Marketing

• For contingent crop planning

For weather based precision farming For weather based precision farming Requirements are N l ti th l t lik t t i f ll RH − Near real time weather elements like temperature, rainfall, RH, solar radiation etc., − Soil moisture status − Crop condition − Weather forecasts

What farm decisions you will take ? − Sowing − Fertilizer Application − Irrigation Irrigation − Intercultural operation − Plant protection − Plant protection − Harvesting M k ti − Marketing

Grain yield of maize as influenced by methods of irrigation during Kharif, 2007 Kharif, 2007

Irrigation schedule Grain Yield (t/ha) Effective rainfall (mm) Irrigation water (mm) Per cent increase in yield over (t/ha) (mm) (mm) in yield over rainfed

I1 5.58 396.6 0.0

• 1

I2 7.09 396.6 100.0 27.1 I 7 92 396 6 20 0 42 0 I3 7.92 396.6 20.0 42.0 I4 5.94 396.6 23.0 6.5

I1- Rainfed. I2- Ridge and Furrow irrigation ( 0.8 IW/CPE) I D i i i i i ( hi d d ) I3- Drip irrigation as per crop water requirement (every third day). I4- Drought mitigation through drip/life saving irrigation. Vegetative Stage = Dry spell >15 days Reproductive = Dry spell >10 days ( Flowering to dough)

Bowen ratio and energy balance components (MJ m Bowen ratio and energy balance components (MJ m-2

2)in maize during

)in maize during silking stage as influenced by irrigation treatments during Kharif 2009 silking stage as influenced by irrigation treatments during Kharif 2009 g g y g g g g y g g

Trea tmen

%LE of

tmen t

Rn β LE H %LE of Rn I 1 19.57 0.40 13.98 5.59 71.43 I 2 19.57 0.22 16.08 3.52 82.03 I 3 19.57 0.20 16.38 3.19 83.68 I 4 19.57 0.33 14.76 4.81 75.41 I 5 19.57 0.33 14.77 4.80 75.47

I1- Rainfed. I2- Ridge and Furrow irrigation ( 0.8 IW/CPE) I - Drip irrigation as per crop water requirement (every I3- Drip irrigation as per crop water requirement (every third day). I4- Drought mitigation through drip/life saving irrigation. Vegetative Stage = Dry spell >15 days Reproductive = Dry spell >10 days ( Flowering to dough) I5 - Drought mitigation/ life saving irrigation (ridge and furrow method) furrow method) Vegetative Stage = Dry spell >15 days Reproductive = Dry spell >10 days

FASAL Technique Development for Parameter Retrieval FASAL Technique Development for Parameter Retrieval FASAL Technique Development for Parameter Retrieval FASAL Technique Development for Parameter Retrieval and Crop Growth Simulation and Crop Growth Simulation

Energy components in low land rice at RARS, Jagtial during Kharif 2009

IOP dates Rn

β

LE H %LE of Rn 10-Aug 11.19 0.40 8.01 3.18 71.50 22-Sep 12.15 0.37 8.89 3.25 73.20 01-Oct 8.06 0.23 6.58 1.48 81.60 14-Oct 11.53 0.29 8.93 2.60 77.50 18-Nov 6.83 0.39 4.92 1.91 72.10

Studies on influence of weather factors on growth and yield of Samba Mahsuri using CERES model

10 12 8 a-1)

Simulated grain yield (t/ha) of rice under

4 6 Yield (t ha

rice under different climate change scenarios

2 Y

scenarios

S1 S2 S3 S4 S5 S6 S7 S8 S9 Scenario

S1 – Normal S2 - Increase in maximum and minimum temperature by 1oC S Increase in ma im m and minim m temperat re b 2oC S6 – S2+S4 S7 – S3+S4 S S +S S3 - Increase in maximum and minimum temperature by 2oC S4 - Increase in CO2 level to 450 ppm S5 - Increase in CO2 level to 600 ppm S8 – S2+S5 S9 – S3+S5

Combined effect of changes in temperature and CO2 levels

• n grain yield of Rabi jowar

Changes in

• n grain yield of Rabi jowar

Changes in temperature(0C) and CO2 at diff t l l Simulated grain yield (k h

1)

% Ch f N l different level (kgha-1) % Change from Normal 450 ppm Early Timely Late Early Timely Late 1 4136 4717 5207

• 12
• 12
• 9

Normal 4695 5360 5727

• 1

5390 6302 6230 15 18 9 600 ppm Early Timely Late Early Timely Late 1 4572 5185 5606

• 12
• 11

3 Normal 5169 5826 5449

• 1

5866 6512 6463 13 12 19

Comparison of rice grain yield (kg/ha) simulated by CERES rice with actual yield during rabi 2010-11

Actual rabi yield 2010 Mid season End season

actual yield during rabi 2010-11

5000 6000 4000 5000 g/ha) 2000 3000 Yield (kg 1000 2000

Observed and simulated yields (kg/ha) of paddy in diff t di t i t f A dh P d h d i bi 2010

Mid season Harvest

different districts of Andhra Pradesh during rabi 2010- 11

Mid season Harvest Districts Actual Simulated %Deviation Actual Simulated %Deviation East Godavari 4883 3606

• 35

4883 3676

• 33

West Godavari 4584 3606 27 4584 3821 20 West Godavari 4584 3606

• 27

4584 3821

• 20

Krishna 3886 3733

• 4

3886 4035 4 Warangal 3389 4008 15 3389 4012 16 Mahabubnagar 2804 2331

• 20

2804 3935 29 Nizamabad 3899 3544

• 10

3899 5020 22 Karimanagar 3770 4008 6 3770 4131 9 g Mean 3888 3548

• 11

3888 4090 4 RMSE 615 754 NRMSE 16 19 NRMSE 16 19 Note: NRMSE <10% Excellent, >10 to <20 good, >20 to <30 Fair, >30 poor

Scenario analysis using APSIM irrigate on soil water deficit vs irrigate on fixed days after seeding irrigate on soil water deficit vs. irrigate on fixed days after seeding

Total amount of irrigation water used in both cases ~ 200 mm

Test-bed at ANGRAU, Hyderabad

Experiments on

Precision Irrigation

Flux tower in maize field Sensors Air temperatue Sensors RH Radiation CO2 conc. Soil temperature and p Soil moisture Field Server Sensors Air temperature Relative humidity and Relative humidity and CO2 concentration

Profiles of air temperature, RH and CO2 concentration over maize field during Kharif 2010

Bowen ratio and energy fluxes measured using wireless sensor network in maize field during Kharif 2010

P t tili ti f t

Variation of Bowen ratio

Per cent utilisation of net radiation in ET Available net radiation and energy Daily rain fall during the crop Available net radiation and energy fluxes season

WEATHER INSURANCE

The basic idea of weather insurance is to estimate the percentage deviation in crop output due to adverse weather conditions. Unlike regular insurance, hi h ld l h i l d th i t t which would only cover physical damage, weather insurance protects against additional expenses or loss of profit from specific bad weather events. An analysis of Indian Crop Insurance Program between 1985 and 2003 reveals that rainfall accounted for nearly 95 percent claims 85 percent reveals that rainfall accounted for nearly 95 percent claims – 85 percent because of deficit rainfall and 10 percent because of excess rainfall. Financial protection based on the performance of specified index in relation to a specified trigger. Weather indices could be deficit/excess rainfall,extreme fluctuations of temperature, relative humidity and/or a combination of above.

Loss payment through weather insurance

Stages

Presowing Seedling Vegetative Reproductiv e Maturity

Payout:

t Time

11-24Jun 25Jun- 15Jul

16Jul-26Aug

27Aug-7Oct 8Oct-11Nov 2 weeks 3 weeks 6 weeks 6 weeks 5.5 weeks

Payout:

(Strike – actual rainfall) * Notional, e.g. INR 20 / mm

40-60mm 50-70mm 170-190mm 180-200mm 40mm

Water requirement Retention r

180mm 190mm

Strike

60mm 40mm 70mm 70mm 90mm 70mm

Strike Strike

50mm 40mm 70mm

Rainfall required Actual Rainfall recorded Output Loss payment

Source: Vivek Pawale Galileo Weather Risk Management Ltd

WEATHER BASED AGRO-ADVISORIES

Weather based Weather based agro-advisories Website agromet ap nic i agromet.ap.nic.i n

AAS bulletin dissemination

Agrometeorological Advisory* Services in ANGRAU

AAS Bulletin

AAS Unit Districts

AAS Bulletin

AAS Unit Districts Agromet–Cell Ranga Reddy, Medak Rajendranagar Mahabubnagar & N l d Agril.Minister Agril.Sec. C & DA

Univ officers

Nalgonda RARS, Jagtial Karimnagar, Warangal, Khammam, Adilabad &

Chief Sec. Print & Electronic

JDAs

• Univ. officers

Agromet Website & , Nizamabad RARS, Anakapalle Visakhapatnam, Vi i d

Electronic media

AOs AGRISNET Vizianagaram and Srikakulam RARS, Chintapalli High altitude &Tribal

Farmer

AEO s , p g areas RARS, Lam Guntur, Krishna, Prakasam KVKs, DAATTCs,

Farmer

Prakasam, East & West Godavari RARS, Tirupati Chittoor, S.P.S. Nellore and , NGOs, Farmers clubs p Kadapa A.R.S, Anantapur Anantapur and Kurnool

*Issued on every Tuesday and Friday valid for next 4 days

Project Blackboard and posted agro advisories at Project Blackboard and posted agro-advisories at the Bairanpally Grampanchayat in Warangal district