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

India’s Agril. Land and Food production I di ’ A il L d d F d d ti Rainfed agriculture( 6 0 % ) Rainfed agriculture( 6 0 % ) (45% food production) (85 Mha) (85 Mha) (57 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 − Near real time weather elements like temperature, rainfall, RH, N l ti th l t lik t t i f ll 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 − Marketing M k ti

Grain yield of maize as influenced by methods of irrigation during Kharif , 2007 Kharif , 2007 Irrigation Grain Effective Irrigation Per cent schedule Yield rainfall water increase (t/ha) (t/ha) (mm) (mm) (mm) (mm) in yield over in yield over rainfed I 1 5.58 396.6 0.0 - 1 I 2 7.09 396.6 100.0 27.1 I I 3 7 92 7.92 396 6 396.6 20 0 20.0 42 0 42.0 I 4 5.94 396.6 23.0 6.5 I 1 - Rainfed. I 2 - Ridge and Furrow irrigation ( 0.8 IW/CPE) I I 3 - Drip irrigation as per crop water requirement (every third day). D i i i i i ( hi d d ) I 4 - 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 -2 2 )in maize during Bowen ratio and energy balance components (MJ m )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 g g y y g g g g Trea %LE of %LE of tmen tmen β Rn LE H Rn t 19.57 13.98 5.59 71.43 I 1 0.40 19.57 0.22 16.08 3.52 82.03 I 2 19.57 16.38 3.19 83.68 I 3 0.20 19.57 14.76 4.81 75.41 I 4 0.33 19.57 14.77 4.80 75.47 I 5 0.33 I 1 - Rainfed. I 2 - Ridge and Furrow irrigation ( 0.8 IW/CPE) I - Drip irrigation as per crop water requirement (every I 3 - Drip irrigation as per crop water requirement (every third day). I 4 - Drought mitigation through drip/life saving irrigation. Vegetative Stage = Dry spell >15 days Reproductive = Dry spell >10 days ( Flowering to dough) I 5 - 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 %LE of β dates Rn LE H 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 12 10 Simulated grain 8 yield (t/ha) of a-1) rice under rice under Yield (t ha 6 different climate change 4 scenarios scenarios Y 2 0 0 S1 S2 S3 S4 S5 S6 S7 S8 S9 Scenario S 1 – Normal S 6 – S 2 +S 4 S 2 - Increase in maximum and minimum temperature by 1 o C S 7 – S 3 +S 4 S 3 - Increase in maximum and minimum temperature by 2 o C Increase in ma im m and minim m temperat re b 2 o C S S S 8 – S 2 +S 5 S +S S 4 - Increase in CO 2 level to 450 ppm S 9 – S 3 +S 5 S 5 - Increase in CO 2 level to 600 ppm

Combined effect of changes in temperature and CO2 levels on grain yield of Rabi jowar on grain yield of Rabi jowar Changes in Changes in temperature( 0 C) and CO 2 at Simulated grain yield (kgha -1 ) 1 ) different level diff t l l (k h % Change from Normal % Ch f N l 450 ppm Early Timely Late Early Timely Late 1 4136 4717 5207 -12 -12 -9 Normal 4695 5360 5727 0 0 0 -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 0 0 0 -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 yield during rabi 2010-11 Actual rabi yield 2010 Mid season End season 6000 5000 5000 g/ha) 4000 Yield (kg 3000 2000 2000 1000 0

Observed and simulated yields (kg/ha) of paddy in different districts of Andhra Pradesh during rabi 2010- diff t di t i t f A dh P d h d i bi 2010 11 Mid season Mid season Harvest Harvest Districts Actual Simulated %Deviation Actual Simulated %Deviation East Godavari 4883 3606 -35 4883 3676 -33 West Godavari West Godavari 4584 4584 3606 3606 -27 27 4584 4584 3821 3821 -20 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 g 3770 4008 6 3770 4131 9 Mean 3888 3548 -11 3888 4090 4 RMSE 615 754 NRMSE NRMSE 16 16 19 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

Precision Irrigation Experiments on

Flux tower in maize field Sensors Sensors Air temperatue RH Radiation CO 2 conc. Soil temperature and p Soil moisture Field Server Sensors Air temperature Relative humidity and Relative humidity and CO 2 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 Per cent utilisation of net t tili ti f t Variation of Bowen ratio radiation in ET Daily rain fall during the crop Available net radiation and energy Available net radiation and energy season fluxes

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, which would only cover physical damage, weather insurance protects hi h ld l h i l d th i t t 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 reveals that rainfall accounted for nearly 95 percent claims – 85 percent 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 Reproductiv Maturity Seedling Vegetative e Payout: Payout: 11-24Jun 25Jun- 27Aug-7Oct 16Jul-26Aug Time 8Oct-11Nov (Strike – actual rainfall) 15Jul * Notional, 2 weeks 3 weeks 6 weeks 6 weeks 5.5 weeks e.g. INR 20 / mm t requirement Retention Water 40-60mm 50-70mm 170-190mm 180-200mm 40mm r 190mm 180mm Strike Strike Strike 70mm 70mm 90mm 60mm 40mm 40mm 70mm 70mm 50mm Output Rainfall required Actual Rainfall recorded 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