Climate Branch

Predictions Of Optimal Chickpea Flowering Time For Better Yield

Muhuddin R. Anwar, Yashvir S. Chauhan, Mark Richards, David J. Luckett, Rosy Raman, Neroli Graham, Kristy Hobson

Australian chickpea industry: expansion of areas due to higher prices profitable options to break disease cycles value to farmers ~$2 B however, has a high risk of chilling temperatures (<15oC) results in flower and pod abortion and subsequent yield loses

Overview:• identify and understand the physiological drivers of chilling sensitivity in chickpea• determine the limitations and the benefits of improving tolerance• improved knowledge about the response of current and new varieties

Germplasm, genetics, agronomy, physiology, modelling, and field experiments

GRDC Bilateral: BLG111, 2018-2022Does improving chilling tolerance of chickpea increase and stabilize yield and improve farming system ‘fit’?

Efficacy of the Agricultural Production Systems Simulator (APSIM): to simulate chickpea flowering time

• Divergence in flowering time• Accounting for soil water, photoperiod and temperature is superior in predicting flowering timeTTm = TT * (1.65 – FASW) (when FASW ≥ 0.65 and the chickpea stage ≥ 3)(Chauhan et al. 2019)

Simulating flowering time

Thermal time (tt) to last flowering (°Cd) at Roseworthy (Victor’s data)Sowing Date Cultivar Observed tt(°Cd) Simulated tt(°Cd)07-June 2013 PBA HatTrick 1452 149709-July 2013 PBA HatTrick 1479 144310-June 2014 PBA HatTrick 1419 148415-July 2014 PBA HatTrick 1216 1311

Wagga (2016, 2017, 2018), Tamworth (2018), Yenda (2016)

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e.g. Wagga (TOS1) biomass

APSIM (Wagga 2018)

Sites included in the simulation study: plant available water capacity (PAWC), mean rainfall (mm) and temperature (oC) from 1957-2018. A-O is the months from April to October and the coefficient of variation (CV%) is in parentheses.

Mean annual rainfall Mean A-O temperatures (T) (oC)

Location PAWC (mm) Annual A-O Mean T MaxtT MinTEMERALD 287 592 (32%) 195 (60%) 19.5 (3.9%) 26.9 (3.0%) 12.2 (7.9%)

BILOELA 140 640 (26%) 225 (41%) 18.1 (3.7%) 26.0 (3.2%) 10.1 (10.0%)

ROMA 119 592 (28%) 235 (44%) 16.5 (3.8%) 24.4 (3.5%) 8.6 (11.7%)

DALBY 285 625 (23%) 253 (39%) 15.7 (3.8%) 23.2 (3.8%) 8.2 (10.1%)

HERMITAGE 216 707 (23%) 289 (37%) 13.6 (4.5%) 20.6 (4.2%) 6.6 (14.4%)

MUNGINDI 181 513 (29%) 220 (43%) 16.2 (4.3%) 23.7 (4.3%) 8.7 (9.0%)

MOREE 196 588 (25%) 253 (41%) 15.3 (4.0%) 22.4 (4.3%) 8.1 (9.7%)

BELLATA 198 634 (25%) 279 (38%) 14.9 (3.9%) 22.3 (3.9%) 7.5 (10.0%)

WALGETT 211 467 (32%) 219 (43%) 15.5 (3.3%) 23.0 (3.8%) 8.0 (10.9%)

TAMWORTH 245 667 (24%) 312 (33%) 13.2 (3.6%) 20.1 (5.0%) 6.4 (13.5%)

YENDA 165 432 (33%) 254 (38%) 12.9 (3.9%) 19.2 (4.2%) 6.5 (9.6%)

WAGGA 110 541 (28%) 328 (35%) 11.6 (4.0%) 17.5 (5.2%) 5.8 (12.2%)

TAMWORTH WAGGA WALGETT YENDA

HERMITAGE MOREE MUNGINDI ROMA

BELLATA BILOELA DALBY EMERALD

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The number of extra days from original to revised flowering time in APSIM simulations. A: Histograms of extra days to flower by revised method from 12 locations and (B): extra days to flower by revised method in individual sites during 62 years (1957-2018)

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The mean increase was 15.5 days (1 to 67 days)

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Comparison between original flowering time and revised (based on the additional effect of soil water) flowering time predicted by APSIM in 12 sites during 62 years (1957-2018). The solid line is the 1:1 line passing through zero

TAMWORTH WAGGA WALGETT YENDA

HERMITAGE MOREE MUNGINDI ROMA

BELLATA BILOELA DALBY EMERALD

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Differences in flowering date The greatest range in revised predicted first flowering date was seen

in the warmer and more northern Queensland sites of Biloela, Emerald and Roma

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Flowering Last flower

Date of either flowering or last flower (i.e., start of grain-fill) for 62 years (1957-2018) across 12 sites

Location was a strong determinant of phenology

Difference (days) between medians for floral initiation (FI) and last flowering date or start of grain-fill for 62 years (1957-2018) across 12 sites

Tamworth had the longest duration (~49 days) in contrast to Mungindi, Dalby and Wagga which had much shorter durations (~31 to 34 days),with the other sites being intermediate

BELLATA

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The relationship of total rainfall during the period from floral initiation (FI) to the end of grain-fill (PF) with chickpea yields for all 12 sites during 62 years (1957-2018); the blue line is the polynomial regression line

which showed a significant, positive (polynomial) relationship (p<0.001)

Location-wise relationship of total in-season rainfall and chickpea yield during 62 years (1957-2018)

Predicted yield is highly responsive to total in-season rainfall

TAMWORTH WAGGA WALGETT YENDA

HERMITAGE MOREE MUNGINDI ROMA

BELLATA BILOELA DALBY EMERALD

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Total rainfall from sowing to maturity (mm)

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• Location-wise relationship between maximum plant available soil water (PAWC) and flowering period (from first to last flower) for 62 years

• MPAWC during flowering was associated with a general increase in duration of flowering, but not at all sites. The relationship was pretty flat in Queensland sites like Biloela, Dalby and Emerald

TAMWORTH WAGGA WALGETT YENDA

HERMITAGE MOREE MUNGINDI ROMA

BELLATA BILOELA DALBY EMERALD

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Maximum PAWC from Flowering to Last flower

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• Location-wise relationship between average daily temperature during the flowering period and duration of flowering days (from first to last flower) for 62 years (1957-2018)

• Higher temperatures drive shorter flowering duration, presumably by bringing on terminal drought and damaging fruiting structures (flowers and young pods)

TAMWORTH WAGGA WALGETT YENDA

HERMITAGE MOREE MUNGINDI ROMA

BELLATA BILOELA DALBY EMERALD

12 15 18 21 12 15 18 21 12 15 18 21 12 15 18 21

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Average daily temperature from Flowering to Last fl

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• Variability of simulated chickpea yields during 62 years (1957-2018) across 12 sites.• Variation depending on climate and soil types (PAWC); although these yield values

are similar to average yield reported in the NVT trials

Chilling indices analysed by Classification and Regression Tree (CART)Fifteen chilling temperature indices calculated from flowering period (first flower to last flower)Index Description Unit

t2 Count of daily minimum temperatures ≤ 2°C Dayst3 Count of daily minimum temperatures > 2°C and ≤ 3°C Dayst4 Count of daily minimum temperatures > 3°C and ≤ 4°C Dayst5 Count of daily minimum temperatures > 4°C and ≤ 5°C Dayst6 Count of daily minimum temperatures > 5°C and ≤ 6°C Dayst7 Count of daily minimum temperatures > 6°C and ≤ 7°C Dayst8 Count of daily minimum temperatures > 7°C and ≤ 8°C Dayst9 Count of daily minimum temperatures > 8°C and ≤ 9°C Dayst10 Count of daily minimum temperatures > 9°C and ≤ 10°C Dayst11 Count of daily minimum temperatures > 10°C and ≤ 11°C Dayst12 Count of daily minimum temperatures > 11°C and ≤ 12°C Dayst13 Count of daily minimum temperatures > 12°C and ≤ 13°C Dayst14 Count of daily minimum temperatures > 13°C and ≤ 14°C Dayst15 Count of daily minimum temperatures > 14°C and ≤ 15°C Dayst16 Count of daily minimum temperatures > 15°C and ≤ 16°C Days

Binned into four groups1. T2_4, if 2⁰C ≤ T ≤ 4°C2. T5_8, if 4°C < T≤ 8⁰C3. T9_12, if 8°C < T ≤ 12°C4. T13_16, if 12°C < T ≤ 16°C

The frequency of the these temperatures vary from year to year and locations, sothese four groups were further partitioned into three sub-groups based onfrequencies of 25, 25-75 and more than 75 percentile. This was categorised as“Low”, “Mid” and “High”.

• 15 chilling temperature indices• relationship was complex and non-linear• CART model (De’ath & Fabricius, 2000)

CART is easy to: (1) interpret the results, (2) assess the predictor variables for their degree of influence, and (3) handle the higher order interactions between multiple predictors (Elith et al., 2008)

• Each box contains the mean value of the group (yield in kg/ha)• Left of each split = “yes”, and to the right =“no”• Split (2) conditioned by t13-16 group: Low & Mid (yes: ~7%)• Split (3) at t13-16 group: High (no: Î 2998 )• Split (4) at t9-12 group: Low & Mid (yes: ~7% )• Split (5) at t9-12 group: High(no: Î 2785)• Split (8) at t2- 4 group: High & Low (yes: ~9%)• Split (9) at t2-4 group: Mid (no: Î 2576)

Hermitage yield (kg/ha)

Classification and Regression Tree (CART)

Conclusions:

• Accurate matching of phenology can improve adaptation of crops to adverse conditions, but achieving this in practice has been limited in chickpea due to lack of accurate knowledge of drivers of crop phenology.

• Accounting soil water, with photoperiod and temperature improves prediction of flowering time

• Additional analysis performed in this study has resulted in a greater understanding of variation in yield resulting from low temperature and rainfall

• Further work on understanding the source of temporal variation in yield is required

• Progress in these two areas should eventually lead to development of decision support system that will enable better matching of phenology to achieve higher yield by reducing stresses related to soil water and temperature

• Ongoing field experiments at Kingaroy (Qld), Wagga, Yanco, & Narrabri (NSW) may further improve model predictions