Dealing with climate and yield variability: the role of precision agricultural technologies and crop models

Transcription

1 Dealing with climate and yield variability: the role of precision agricultural technologies and crop models Bruno Basso 1, G.Phillip Robertson 1 and Jerry Hatfield Montpellier March 16-18, 2015

2 Yield Results from Complex Spatial and Temporal Interactions Management Soiltype Weather Cultivars Insects Yield Fertility Weeds Diseases Plant Stand Decision making in today s agricultural environmental is a volatile process (i.e. prices of products and input, weather).

3 Variability Spatial Temporal Static Properties (soil texture, ph etc..) Dynamic Prop. (within the yearsoil water content nitrates) Variation (from year to year)

4 6 Years of Yields in a Single Field Frequency, % t/ha: 1 bu/acre: Yield t/ha bu/acr e

5 Stability map

6 Primary Greenhouse Gases Global Warming Potential Agriculture is directly responsible for approximately 10% 14% of total annual global anthropogenic greenhouse gas emissions Carbon dioxide (CO 2 ) = 1 Methane (CH 4 ) = 21 Nitrous oxide (N 2 O) = 296 Chloroflurocarbons Ozone Water vapour N 2 O CH 4 Agriculture Robertson, 2014

7 Hypoxia in Lake Erie Satellite image of 2011 bloom, the worst bloom in recent years, which impacted over half of the lake shore. (Credit: MERIS/ESA, processed by NOAA/NOS/NCCOS)

8 The approach Historical Records Management Sowing Tillage etc. Model inputs : Soil Plant etc. CROP MODELS Extended synthetic time series Phenological development Variable N management Decision criteria Tactical or Strategic N management

9 From strategic to tactical approach using crop models The strategic focuses on a long term objective The tactical addresses a within season decision Inter year analysis Intra year analysis Recorded climate = 0 Recorded climate D day Recorded climate D day

10 Developing a fertilization strategy 1.00 Cumulative Probability Function kg N/ ha 100 kg N/ ha 150 kg N/ ha 200 kg N/ ha 250 kg N/ ha Maize Grain Yield (t ha-1) Cumulative probability kg N/ha 100 kg N/ha 150 kg N/ha 200 kg N/ha 250 kg N/ha Net Revenue (Euros ha-1)

11 Measured profit 2011 Green: Profit Red: Lower Profit Avg: $740/acre

12 Measured profit 2012 Green: Profit Red: Loss Avg: $194/acre

13 SALUS Best Nitrogen Management 2012 Green: Profit Red: Loss Avg: $428/acres 2012, Only 30 # N/ac

14 SALUS Irrigation 2012 Green: Profit Red: Loss Avg: $980/acres 2012, Irrigated 200 # N/ac

15 Years SIMULATED MEASURED Corn Grain Yields (Mg/ha)

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18 Decision making process The approach Climatic scenarios N scenarios Decision criteria Agronomical criteria: Maximizing yield Economical criteria: Maximizing farmers Marginal Net Revenues Y. G N N MNR. N P P Y N = Grain yield under N practice [ton.ha 1 ] G P = Grain selling price [.ton 1 ] N = N fertilization amount [kgn.ha 1 ] N P = N price [. kgn 1 ] Environmental criteria: Minimizing the Nitrogen Available for Leaching at the end of the season Basso, B.et al., European Journal of Agronomy 35(2)

19 Management zones Tomography High Yield Zone Medium Yield Zone Yield Low Yield Zone

20 N management using spatially explicit crop modeling Net Revenue ($ ha -1) High Yield Zone kg N ha -1 Medium Yield Zone Low Yield Zone 60 kg N ha kg N ha Nitrate Leaching (kg N ha -1 ) Dual criteria optimization through tested model determines the N rate that minimizes nitrate leaching and increases net revenues for farmers (Basso et al., 2011; Eur J. Agron 35: )

21 1200 Tactical approach for optimal N rates Marginal Net Revenue ( ha 1) LYZ > 34 mm (60 N) MYZ < 50 mm (60 N) MYZ > 50 mm (90 N) HYZ < 50 mm (60 N) HYZ > 50 mm (120 N) Plant available soil water at sidedressing (mm)

22 Visable image taken on August 13 using MSU drone

23 Multispectral and laser scanner image taken from MSU drone

24 Net surface flow (Runon Runoff) in SALUS crop model (cm)

25 Observed Variable Rate Nitrogen Variable rate nitrogen rates were selected using SALUS model after evaluating the N response for each of the N rate in each zone for 30 years of available weather 9 8 zone 1 zone 2 zone yield (t/ha) kg/ha 190 kg/ha (control) 170 kg/ha 190 kg/ha (control) 150 kg/ha 190 kg/ha (control) Basso et al., in preparation (Basso et al. in preparation)

26 Conclusions Precision agriculture when integrated with crop modeling can help farmers: adapt to climate variability and increase their resources use efficiency Gains in energy efficiency for farm operations that consume fuel, including mechanical operation such tillage, irrigation, fertilization etc.. Gains in production or yield efficiency for grain, and other agricultural products Abetment of the GHC emission (N 2 O) by better fertilizer use