Crop response to water stress: eco-physiological and proximate sensing techniques

Transcription

1 ACLIMAS training courses Advanced tools to predict water stress and its effect on yield Hammamet (Tunisia) 24-27/11/2014 Crop response to water stress: eco-physiological and proximate sensing techniques Mladen Todorovic, Vito Buono, Carlo Ranieri (CIHEAM IAMB)

2 1. Introduction 2. Thermal camera and CWSI Principles Calculation of the CWSI (Excel program) 3. Capacitance sensors Principles Field calibration 4. Examples of application Detecting the level of water stress with sensors Examples for wheat, potato, olive

3 Measurement of plant water status Measurement of soil water status Crop-soil water balance calculation

4 1. Introduction 2. Thermal camera and CWSI Principles Calculation of the CWSI (Excel program) 3. Capacitance sensors 4. Examples of application Detecting the level of water stress with sensors Examples for wheat, potato, olive

5 Energy balance of a leaf/canopy

6 Measuring surface temperature Non-imaging devices or infrared thermometers (IRTs) can be portable hand-held temperature guns, or continuously monitoring cylindrical standalone sensors that can be connected to a data logger (can be installed permanently in the field). On the other hand, the measured T is often a composite of vegetation and background (soil/sky) temperatures, which makes the interpretation difficult and can cause large estimation errors. Imaging devices or thermal cameras are more delicate and are much more expensive than IRTs. They provide images, thus they are very precise.

7 Temperature-based indexes (Tc Ta), is the canopy temperature depression (CTD), is the most common normalization of Tc and is used widely as an indicator of plant heat stress tolerance or drought stress in crops, often for studying crops experiencing different irrigation regimes; it has also been used intensively for wheat cultivar selection and studies of water stress in other crops (apple, cherry, grapevine, peach, and citrus) often shows a good correlation with irrigation treatments, leaf water potential, or somatl conductance (gs).

8 Stress degree days (SDD) Irrigation scheduling based on (Tc Ta) has mostly occurred in the form of the stress degree day (SDD) method. Irrigation is started as soon as SDD exceeds 0. SDD worked particularly well for wheat. SDD was the first attempt to schedule irrigation based on Tc and was the most widely used thermal index until CWSI was developed. Nowadays, SDD is still occasionally used as a drought stress index. In general, because of the large influence of weather conditions, (Tc Ta) and SDD can only be used in (semi-)arid climates where weather conditions vary little between consecutive days

9 Crop Water Stress Index (CWSI) The crop water stress index (CWSI) is a drought stress index that uses the Ts of a potential (no stress) and dry (completely stress) crop. Both are identical to the real crop, the potential crop is transpiring at maximal rate while the dry crop is not transpiring at all. In CWSI, the normalization is done by Ta and, more importantly, by Tpot and Tdry. This approach is using an upper and lower boundary T.

10 Crop Water Stress Index (CWSI) Different approaches to calculate CWSI were developed: Empirical or baseline approach (CWSIe), the data input is limited to Ta, δe, and Tc. CWSIe requires stable weather conditions (preferably hot and dry, with open skies), and measurements should also be taken only around solar noon Requires local calibration for upper and lower baselines. Direct approach (CWSId), using artificial leaves or treated (covered with water/oil) to simulate the potential and dry conditions Analytical/theorethical approach (CWSIa) In the analytical approach, Tc measurements are combined with meteorological data to compute CWSI.

11 1. Introduction 2. Thermal camera and CWSI Principles Calculation of the CWSI 3. Capacitance/dielectric sensors 4. Examples of application Detecting the level of water stress with sensors Examples for wheat, potato, olive

12 Continuous or discontinuous? In the framework of the HT project, the following sensors have been testes DeltaT PR2 profile probe (DeltaT Devices Ltd, UK), to be used by technicians for discontinuous (time) field monitoring, but with possibly a large number of access points. Decagon ECH2O series (Decagon Devices Inc., USA), for continuous (time) soil recordings, but normally with a limited number of field points; 12

13 1. Introduction 2. Thermal camera and CWSI Principles Calculation of the CWSI 3. Capacitance sensors 4. Examples of application Detecting the level of water stress with sensors Examples for wheat, potato, olive

14 Keywords Crop stages Water stress Deficit irrigation Y, WUE, HI

15 Detection of water stress in wheat

16 Optimizing water productivity In arid and semi-arid regions, water is one of the most limiting factor in crop production, then agricultural research is shifting from maximizing total production to evaluate sustainable methods to increase crop water productivity. Yield response to water varies with location, stress patterns, cultivar, planting dates, and other factors, and many crops have different sensitivities to water stress at various stages of development.

17 Crop sensitivity to water stress According to FAO Paper 66 severe water stress should be avoided during the stages of: tillering - stem elongation (total number of heads and seeds per head is being set) flowering (water deficit will greatly reduce the number of seeds per head) early-mid grain filling (water deficits combined with hot, dry winds will would result in an incomplete grain filling and a reduced yield of poor quality shriveled grains); (also booting to heading stages, according to other authors).

18 How to detect or predict the level of plant stress? Several examples of the application of modelling (Ks) to support deficit irrigation management for wheat can be found in literature. In the case of wheat crop, several specific studies have proven the reliability of the CWSI for monitoring plant water status and support irrigation scheduling.

19 Field experiment at MAIB Typical semi-arid Mediterranean climate, with an annual precipitation of about mm, distributed mostly during autumn and winter seasons while summer in usually hot and dry. Clay-loamy soil; volumetric water content (v/v) at field capacity and wilting point of respectively 37.4% and 23.1%; soil water holding capacity of 143 mm m-1, average soil depth 0.70m. Weather data were collected regularly from the agro-meteorological station placed at MAIB at a distance of about 200 m from experimental fields.

20 Field experiment at MAIB Split-plot, 2 factors (variety, irrigation regime), 3 replications, with water regime as the main plot factor while the varieties as the sub-plot factor; 2 years rainfed rainfed rainfed

21 2 varieties x 3 water regimes 2 durum wheat varieties (Pietrafitta and Vendetta), medium-early cropping cycle, medium productivity, high level of grain quality, both expected to have a good level of drought resistance. 3 water regimes were: o full irrigation (FI), 100% of NIR as determined by the model; o sustained deficit irrigation (SDI), by applying the corresponding 50% of FI volumes; o rainfed (R). Spring cropping cycle, in order to shift most of the cropping cycle under the drier and hotter spring-summer conditions.

22 Stress coefficient (Ks) FAO-56 model, single-kc approach Maximum ETc,max and Adjusted ETc,adj

23 Thermal camera Midday canopy temperature (Tc) was remotely measured at plot scale by means of a thermal camera (mod. FLIR b335, USA), under clear-sky conditions, by pointing the centre of the plot avoiding as much as possible the soil disturbance in the background. The thermal camera was set to return the average temperature of a selected square of the image on the screen, and the four measured values for each plot were then recorded and averaged.

24 Calculating the CWSI The Crop Water Stress Index (CWSI) was calculated according to the empirical method for its estimation. The upper baseline was related with the level of temperature of the rainfed plots, while the lower baseline to the level of the full irrigated ones (Maes and Steppe, Journal Exp Botany, 2012)

25 Linking the CWSI and Ks

26 Weather data The season was cold and wet until the middle of April (50-55 DAS) Total precipitation was mm, with most peaks in March and April. The longest period without any rain was from 6th June (100 DAS) to 3th of July (126 DAS). Tmax Tave Tmin

27 Yield and harvest index 5 Yield 4 0,6 HI 0,5 0,4 3 Pietrafitta 2 Vendetta 1 0,3 Pietrafitta Vendetta 0,2 0,1 0 0 rainfed DI FI rainfed DI FI

28 Water Use Efficiency

29 Crop ETc and Ks ETc curves followed the same trend for all irrigation treatments up to 65 DAS ( booting ), then the rainfed one reduced progressively with respect to others, and at 75 DAS ( flowering ) also the DI. According to the model, at flowering stage, water stress was high for rainfed (Ks ), and moderate for DI (0.20.4), while FI was still unstressed

30 Calculation of the empirical CWSI

31 Curves of CWSI (water regimes x varieties)

32 Level of CWSI Assuming: Low < 0.2; Moderate = ; High = ; Very High = ; Complete > 0.8.

33 Calibrating the model with field observations As a first analyisis, with a trial and error procedure, the following crop parameters were found to have a high sensitivity for model calibration: Rd_max was reduced going from rainfed (0.65 m) to DI (0.60 m) and FI (0.55 m), thus assuming a shallower rooting depth for the irrigated treatments; L_mid was reduced going from FI (40 days) to DI (35) and rainfed (30), assuming a faster start of leaf yellowing and senescence under water stress conditions; Kc_mid values were reduced going from FI (1.2) to DI (1.1) and rainfed (0.9), to consider the differences in crop characteristics (height, LAI, canopy resistance).

34 Detecting soil water deficit in potato

35 Experimental layout The field trial was conducted in the experimental station of MAIB Potato Spunta cv., was planted on the 1st of March water regimes: Full irrigation (I₁₀₀) Deficit irrigation (I₅₀) Rainfed (I₀) The irrigation scheduling was done by the FAO-56 Excel model

36 Experimental measurements Soil water content measurements Amplitude Domain Reflectometry (Soil Moisture Profile Probe PR2/4, DeltaT Devices Ltd, UK): - Soil moisture at 4 depths down to 40 cm - Access tubes were installed on rows between two plants (2/plot) Leaf gas exchange measurements Portable photosynthetic system LI6400 (Li-COR, USA): -Stomatal conductance; - Transpiration rate; Proximate sensing measurements Infra red camera (IRT) (Mod. FLIR B335, USA): Canopy/Leaf temperature (Tc)

37 Simulated soil water depletion (FAO-56)

38 Measured soil water content (PR2 Probe)

39 Comparing PR2 with FAO56

40 Measured soil water content (PR2 Probe)

41 Measured plant temperature

42 Estimated CWSI (empirical)

43 Estimated CWSI (theorethical)

44 Correlation between CWSI and leaf conductance (gs)

45 Demonstration field in IAMB Field In Institute agronomic Mediterranean of Bari (IAMB) in southern Italy under typical Mediterranean semi-arid climate. Rainy season : November to April (annual rainfall is about 500mm) Dry season : May to October Olive tree Variety : Olea europaea L. They were planted about 30 years ago. Trees height is 4-5 m and diameter is about 20 cm at 0.5 m from ground.

46 Positioning of access tubes Profile Probe (PR2/4, Delta-T Devices Ltd.) Hand logger (HH2, DeltaT Devices Ltd.)

47 Critical threshold soil water content (2013)

48 Depletion (mm) /06/ /06/ /07/ /07/ /07/ /08/ /08/ /08/ /09/ /09/ /06/ /06/ /07/ /07/ /07/ /08/ /08/ /08/ /09/ /09/ /04/ /04/ /04/ /06/2014 De mes D50 10/06/ /05/2014 Depl_D50% 31/05/2014 RAW [mm] 21/05/2014 TAW [mm] 21/05/ /05/ /05/ /05/ /05/ /04/ /04/ /04/ /03/ /03/2014 Depletion (mm) Comparing FAO-56 model and (2014) TAW [mm] RAW [mm] Depl_D+R De mes D+R