Spatial patterns of estimated optimal flowering period of wheat across the southwest of Western Australia

Field Crops Research 247 (2020) 107710

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Spatial patterns of estimated optimal flowering period of wheat across the southwest of Western Australia

T

Chao Chena,*, Andrew L. Fletchera, Noboru Otab, Bonnie M. Flohrc, Julianne M. Lilleyd, Roger A. Lawesa a

CSIRO Agriculture and Food, Private Bag 5, Wembley, WA 6913, Australia CSIRO Health & Biosecurity, GPO BOX 1700, Canberra, ACT 2601, Australia c CSIRO Agriculture and Food, Locked Bag 2, Glen Osmond, SA 5064, Australia d CSIRO Agriculture and Food, GPO BOX 1700, Canberra, ACT 2601, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Optimal flowering period Wheat Heat Frost APSIM

In order to maximize wheat yield, farmers need to match sowing date and cultivar phenology to ensure that flowering occurs within the optimal flowering period, which minimizes the combined impacts of frost, heat and water stress on grain yield. The optimal flowering period is location specific and so far the spatial patterns of optimal flowering period have not been defined. This study conducted a simulation analysis using multiple sowing dates, long-term (1967–2007) and fine-scale gridded (0.05° × 0.05°) climate data. The simulations were used to identify the spatial patterns of the optimal flowering periods (defined as ≥ 95 % of the maximum mean yield occurred during sensitive growth periods) across the southwest of Western Australia. The results showed that optimal flowering periods of wheat were mainly determined by the spatial pattern of climate, with the cultivar and soil type playing only a minor role. There were clear geographical patterns of the optimal flowering periods across the wheatbelt of Western Australia, with opening and closing dates of the optimal flowering periods being earlier (23 Jul–30 Sep) in the northeast and later (23 Aug–21 Oct) in the southwest. There was a relatively small difference in the duration of the optimal flowering period across most areas of the wheatbelt. Provided flowering occurs within the optimal flowering period, a slow-developing cultivar sown early would achieve higher yield, due to longer period to accumulate biomass, than a fast-developing cultivar sown late. These results provide Western Australian growers with knowledge of how to match sowing date and cultivar to achieve flowering within the optimal flowering period.

1. Introduction Phenological development is an important trait determining crop adaptation to harsh and/or highly erratic environmental conditions like those in Australia (Gouache et al., 2012; Langer et al., 2014). For wheat, the most widely grown grain crop in Australia, even a short period of low temperature (Gibson and Paulsen, 1999; Fuller et al., 2007) or high temperature (Tashiro and Wardlaw, 1990; Ferris et al., 1998) around flowering can cause a severe reduction in the potential grain number. This inevitably reduces final yield and causes significant economic losses (Boer et al., 1993; Fuller et al., 2007; Luo et al., 2018). It is critical for a wheat crop to flower during the optimal flowering period (OFP) (Flohr et al., 2017) to minimise the effects of drought, frost and heat stresses on yield, which is influenced by a combination of environmental factors such as temperature, precipitation, solar

⁎

radiation and soil type (Flohr et al., 2017). As a result, the opening, closing and duration of the OFP varies from season-to-season and from location-to-location. In reality, for a given location farmers aim for an OFP that corresponds to near maximum (95 % of maximum) yield across a range of seasons (Flohr et al., 2017). The southwestern region of Western Australia (WA, Fig.1) occupies ∼60,000 km2. Rainfed wheat is the most common crop in this region (Asseng et al., 2011; Barlow et al., 2015), with an annual average wheat production of 8.9 million tonnes from 4.7 million ha in the last 5 years (ABARES, 2018). This region contributes ∼50 % of Australia’s export wheat crop, corresponding to 7.6 % of the world’s traded wheat (AEGIC, 2019). The region is characterized by variable soils (Johnston et al., 2003) and a variable climate both spatially and temporally (Asseng et al., 2011). Rainfall is usually the main factor limiting plant production (Ludwig et al., 2009). There is a distinct geographic

Corresponding author. E-mail address: [email protected] (C. Chen).

https://doi.org/10.1016/j.fcr.2019.107710 Received 6 September 2019; Received in revised form 12 December 2019; Accepted 20 December 2019 0378-4290/ © 2019 Published by Elsevier B.V.

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Fig. 1. The study region in southwest of Western Australia. The wheatbelt is indicated by the dark boundary, data sourced from DPIRD (2018). The coloured area shows the low rainfall zone (light green), medium rainfall zone (medium green) and high rainfall zone (dark green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

development, management practices and environmental factors (e.g. sowing date × cultivar × location × year) provide a useful tool to investigate the aspects of extreme climate and adaptation strategies across multiple spatial and temporal scales (Hammer et al., 2006; Chenu et al., 2011; Zheng et al., 2012; Semenov and Stratonovitch, 2013). Studies defining the OFP of a crop and investigating management strategy options to achieve it using simulation models like APSIM (Agricultural Production Systems Simulator) have been reported previously in Australia (Zheng et al., 2012; Bell et al., 2015; Wang et al., 2015; Flohr et al., 2017; Lilley et al., 2019). For example, Zheng et al. (2012) characterized the spatial patterns of frost and heat events across the Australian wheatbelt and determined the safe period for wheat to flower and the corresponding sowing period for 22 selected locations. Their results highlighted the substantial variability of the OFP across locations in a diverse climate across Australia. More recently, Flohr et al. (2017) and Luo et al. (2018) used APSIM to quantify the seasonal effects of water supply and demand and climate factors on wheat yield at selected sites in south-eastern Australia or Australia. Their results showed that the OFPs for wheat were driven by the combined effects of water and temperature stress and the relative importance of each of these factors with location and season. For an environment with variable climate and soils, there exists substantial spatial variability in the OFPs. Defining the spatial patterns of OFP would allow farmers to estimate the OFP at their own locations and thereby better manage their sowing date and cultivar choices. It would also inform breeding programs about appropriate phenological development patterns to target. To date there has been few studies

gradient of rainfall declining from west to east across WA (Fig. 1), with a corresponding gradient in yield potential (Chen et al., 2019). The risk of frost increases from north to south, while that of heat stress vice versa. Hence, an integrated study on the identification of the spatial patterns of the OFP and the development of management strategies (principally cultivar choice and sowing date) is particularly important for improved wheat production in WA. By developing a map of the OFP, it will be possible for farmers across the whole region to identify the OFP for their fields. Managing a crop to flower in the OFP is achieved by matching sowing date with a cultivar with an appropriate rate of phenological development (White et al., 2008; Gouache et al., 2012). However, sowing date for dryland wheat is often limited by the timing of the opening rainfall (Pook et al., 2009). Therefore, the choice of cultivar duration should match the available sowing dates which vary from year-to-year. An appropriate combination of sowing date and cultivar is crucial to allow flowering to occur during the OFP (Dennett et al., 1999; Zheng et al., 2012; Flohr et al., 2018a). Studies that define the OFP can be conducted in field conditions and much has been learnt regarding understanding the OFP and recommendations to farmers about how to achieve flowering within this OFP with different cultivars and different sowing dates (Anderson et al., 1996; Dennett et al., 1999). However experimental results are location and season specific. Furthermore, field experiments are time consuming, relatively expensive and difficulties in up-scaling from limited numbers of observations are apparent (Singh et al., 2006). Crop simulation models that capture interactions between crop growth and 2

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conducted to define the spatial patterns of the OFPs for wheat across the wheatbelt of WA. The principal aim of this work is to characterize the geographical pattern of the OFPs over multiple seasons across the wheatbelt of WA. Using a crop modelling approach, this study aimed to: 1) map the spatial patterns of OFPs of wheat across the wheatbelt of WA; 2) spatially characterize the occurrence of frost risk, heat stress and soil moisture stress that determine the OFPs; and 3) explore the combination of cultivar and sowing date to achieve the OFP. We hypothesized that the OFP opened and ended earlier in the northeast compared to the southwest of wheatbelt. We also hypothesized that the duration of the OFP would be longer in the southwest of the wheatbelt due to less drought stress than in the northeast.

Table 2 Phenological parameters for an existing fast-developing cultivar (Wyalkatchem) and two slower developing spring wheat cultivars. TTjuv: Thermal time from emergence to end of juvenile phase; TTfi: Thermal time required from end of juvenile phase to floral initiation; TTff: Thermal time from floral initiation to flowering; TTfg: Thermal time from flowering to start of grain filling. Cultivar

Fast Mid Slow

Parameter vern_sens

photop_sens

TTjuv (°Cd)

TTfi (°Cd)

TTff (°Cd)

TTfg (°Cd)

2.0 2.0 2.0

2.7 2.9 3.1

400 425 450

436 461 486

111 111 111

650 650 650

2. Materials and methods the frost, heat and drought factors ensured that OFPs were determined by the effects of temperature, water availability and solar radiation on crop development, growth and yield (Flohr et al., 2017, 2018b).

2.1. The APSIM model APSIM (Holzworth et al., 2014) version 7.9 was used to simulate flowering date and wheat yield in response to climate, soil and sowing date across the wheatbelt of WA. A detailed description of the APSIM model can be found in Keating et al. (2003); Holzworth et al. (2014) and at http://www.apsim.info. The model was found to be robust to predict flowering date with the root-mean-square deviation (RMSD) of 4.0 days and yield, with RMSD of 4 t ha−1 (Asseng et al., 1998). And it has been used to define optimal flowering periods for wheat (Flohr et al., 2017, 2018c; Luo et al., 2018) and canola (Lilley et al., 2019). The current version of APSIM does not account explicitly for the effects of frost and heat on reproductive growth (Barlow et al., 2015). To quantify the effects of the occurrence of frost and heat on wheat yield, which is particularly sensitive to extremely high and low temperatures during reproductive stages, scripts were developed by Bell et al. (2015) and implemented in the APSIM-Wheat model. The reduction on final grain yield was determined by accumulating the effects of multiple heat stress (defined as daily maximum temperature ≥ 32 °C) or frost (defined as daily minimum temperature ≤ 2 °C) events which occurred during Zadok stage 60–79 (Table 1; Zadoks et al. (1974)). The estimated yield reductions caused by heat or frost stress during these sensitive stages of yield development were calculated by 3 stages of stress. Mild (0–2 °C), moderate (-2 to 0 °C) and severe (< -2 °C) frost events reduced yield by 10, 20 and 90 %, respectively for each day of stress. Mild (32–34 °C), moderate (34–36 °C) and severe (> 36 °C) heat events reduced yield by 10, 20 and 90 %, respectively for each day of stress. Drought was referenced to soil moisture stress (SMS) from Zadok stage 15 to Zadok stage 79. In the APSIM-Wheat model, SMS was calculated as the difference between 1 and soil moisture stress index that was defined as the minimum value of 1 and the ratio of soil water supply to potential water demand based on Lobell et al. (2015). The effect of SMS on wheat yield was mediated through the decrease in biomass accumulation and substantial retranslations among plant organs (Chenu et al., 2013). Drought is considered to occur if SMS is ≥ 0.3, with greater SMS values indicating more severe drought stress (Chenu et al., 2013). The range of sowing dates simulated along with

2.2. Confirming the role of wheat cultivars in determining the optimal flowering period The effect of cultivar duration on the OFP of wheat have been studied in discrete field and/or simulation experiments (Anderson et al., 1996; Flohr et al., 2017). To further confirm the effect of cultivar on the OFPs, spring wheat cultivars that are adapted to WA (Eagles et al., 2014). That is one commonly sown fast-developing spring cultivar (referred to as fast cultivar) that has been parameterized in the APSIM and two virtual cultivars (referred to as mid and slow cultivars) developed based on parameterized fast-developing spring cultivar were used (Table 2, http://www.apsim.info/). For the purposes of this experiment it was not necessary that these hypothetical cultivars represented currently available cultivars. Instead they were used to induce a range of simulated flowering dates based on a wide range of sowing dates. They therefore enabled us to disentangle the complex interaction between weather, sowing time and crop duration and their effect on OFP. Spring wheat cultivars are much less responsive to vernalisation than winter wheat cultivars. Lack of vernalisation slows development but does not block it (Davidson et al., 1985; Bloomfield et al., 2018). Therefore, only the parameters of thermal time to flowering and photoperiod sensitivity were changed to create mid- and slow-developing cultivars (Table 2). Thermal time required for other phenological phases were the same for all three cultivars (Table 2). This gave differences in flowering date from a given sowing date. For example, for a mid season sowing date (10 May) in the south-central wheatbelt (Narrogin), the average flowering date were 2 Sep, 10 Sep and 18 Sep for the fast-, mid- and slow-developing cultivars, respectively (data not shown). In this initial study simulation experiments testing whether the OFP for a given location was affected by cultivar duration were performed at four representative locations across the wheatbelt (Mullewa, Moora, Narrogin and Lake King, Table 3). A major consideration for the location selection is the contrasting rainfall and temperature environments. Mullewa and Moora are located in the low and high rainfall zones in warmer northern wheatbelt, respectively; while Lake King and Narrogin are located in the low and high rainfall zones in the cooler southern wheatbelt, respectively. Two soil types, i.e. sand and clay (Table 4), were used at each location to determine whether there was an effect of soil type. Daily meteorological data at the 4 sites, including maximum and minimum temperatures, rainfall and total solar radiation, were extracted from the SILO data-drill dataset for the period of 1967–2017 (Jeffrey et al., 2001). The three spring cultivars (i.e. fast, mid and slow) were simulated with the same management practices at the 4 selected sites and 2 soils. For each site, the wheat crop was sown at 3-day intervals between 14 Mar and 15 Jul each year. In order to explore the interactive effect of

Table 1 Temperature criteria for frost and heat stress during sensitive stages of yield development (Zadoks et al., 1974) and corresponding daily yield reduction developed by Bell et al. (2015). Yield reduction was calculated daily and accumulated multiplicatively. Temperature (oC)

Stress level

Sensitive Zadoks stages

Daily yield reduction

0–2 −2–0 < -2 32–34 34–36 > 36

Mild Moderate Severe Mild Moderate Severe

60–69 60–75 60–79 60–79 60–79 60–79

10 % 20% 90 % 10 % 20% 30%

3

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Table 3 Mean total annual rainfall, growing-season (Apr–Oct) rainfall, mean annual temperature, and growing-season mean temperature for the four sites used in the study. Data are the means across the 51 years (1967–2017) of the simulations. Site

Latitude (°S)

Mullewa Moora Narrogin Lake King

Longitude (°E)

−28.54 −30.64 −32.93 −33.09

Mean temperature (°C)

115.51 116.00 117.18 119.68

Annual

Growing-season

Annual

Growing-season

20.4 18.9 16.2 16.4

16.5 15.2 12.8 13.3

331 431 472 349

254 355 375 232

spatial resolution of 3″ x 3″ (∼ 90 m × ∼ 90 m). They were based on the system of Schoknecht and Pathan (2013) and grouped from 60 into 10 soil types based on similar plant available water capacity (PAWC) and knowledge of soil experts (Oliver et al., 2009). Due to different soil textures, the PAWC of these soils varied from 76 to 154 mm (Table 4). For each climate pixel, the soil that accounted for the largest area in the pixel was used in the simulations. For each climate grid, the APSIM simulations were conducted using fast-developing cultivar under the same crop management rules as those at above sites to predict flowering date and FHL yield (see Section 2.2). To identify the spatial patterns of extreme climate events in the wheatbelt, the mean frequency of frost, heat and water stress during sensitive growth stages were calculated for each climate grid.

Table 4 Characteristics of the soils used for the simulations of wheat yield with their Western Australian (WA) Soil Group, Australian Soil Classification (Isbell, 1996), plant available water capacity (PAWC) and the proportion of these soils in the wheatbelt of WA. No.

WA Soil Group

Apsoil ID

PAWC (mm)

% of area

1 2 3 4 5 6 7 8 9 10

Calcareous loamy earth Coloured sand Sand Clay Ironstone gravel Sandy earth Shallow sandy duplex Shallow loamy duplex Deep sandy duplex Deep loamy duplex

527 522 520 530 519 523 524 526 525 529

85 123 76 154 85 120 80 118 97 84

3.3 8.0 6.7 5.4 21.0 10.4 4.8 12.7 24.3 3.4

Mean rainfall (mm)

3. Results 3.1. The response of frost-heat-limited yield to flowering time

cultivar and sowing date on flowering date and wheat yield, the opening rainfall for sowing was not considered in the simulations. Instead, 10 mm of irrigation water was applied at sowing to ensure germination. This small amount of water would only have a small effect on simulated yield but ensured that germination occurred. Without this, in some years flowering would have occurred after the OFP due to late sowings only, which would have meant that it was impossible to define the OFP. Each wheat crop was sown at a depth of 30 mm with a plant density of 150 plants m−2. Initial soil water was reset to crop lower limit on 1 Jan every year, assuming maximum water use by previous crop. This was the same approach of Flohr et al. (2017) and coincided with the average observation of soil moisture at crop harvest in a mediterranean-type climate (Monjardino et al., 2015). Soil N was reset annually to a total of 50 kg N ha-1 at sowing, ensuring soil organic matter remained stable over the period of simulation. Soil N was maintained at levels that did not limit yield using the approach of Hochman et al. (2012) and Hochman et al. (2016). As per Flohr et al. (2017), the predicted flowering date and frost and heat limited wheat yield (referred to as FHL yield hereafter) were firstly smoothed with a 15-day running average of all years of simulations at each site or each climate grid (see below). For each location, over the simulations of 51 years (1967–2017) by 42 sowing dates, the smoothed FHL yield was then related to the predicted flowering date. Finally the OFP was defined by flowering dates where FHL yield was ≥ 95 % of the maximum mean FHL yield. More specifically, the OFP refers to the start and end of the date range in which it is optimal to flower to maximise the long-term average FHL yield.

For all cultivars at each location, the long-term (1967–2017) average simulated FHL yield showed a bell-shaped relationship with flowering time (Fig. 2). That is FHL yield of wheat firstly increased as flowering date was delayed until a maximum yield and then decreased as flowering date was further delayed. At Moora and Narrogin, the curves were asymmetrical, with a gentler incline which indicates that low biomass accumulation and frost had a smaller effect on yield early in the growing season, compared with the sharper decline indicating the more severe effects of heat and water stress (Fig. 2c–f). At the other two sites (Mullewa and Lake King), the similar slopes of incline and decline showed that low biomass accumulation, frost, heat and water stress had similar effects on grain yield (Fig. 2a, b, g and h). Slowdeveloping spring cultivars achieved greater average yields than fastand mid-developing cultivars, with the difference between cultivars greater at locations where higher yields were possible. 3.2. Effects of cultivars on the determination of optimal flowering periods Cultivars with different rates of phenological development had only a minor effect on the determination of the OFPs (Fig. 2). For a given location and soil, the opening or closing of the OFPs changed little between the three cultivars with different durations (Fig. 2). For example, at the driest and hottest site Mullewa with a clay soil, the timing of the OFP differed by 1–3 days between the different cultivars (Fig. 2a). The durations of the OFPs were between 16 and 18 days for the three cultivars. At the coldest and wettest site, Narrogin with the clay soil, the open dates of the OFPs were 2 Sep, 4 Sep and 5 Sep for fast-, mid- and slow-developing cultivars, respectively, with close dates occurring on 24 Sep, 25 Sep and 27 Sep for the three cultivars, respectively (Fig. 2e). The durations of the OFP for the three cultivars were 21–22 days. The open and close dates of the OFPs for the two soils were similar for each cultivar at each of the four locations (Fig. 2a–h). In the instance of Moora, the duration of the OFPs for the clay soil was 24 days for the fast-developing cultivar (Fig. 2c), while that for the sand soil was 23 days for the same cultivar (Fig. 2d).

2.3. Mapping spatial patterns of optimal flowering period in the wheatbelt of Western Australia To construct spatial maps of the OFPs defined above, wheat FHL yield and flowering date were obtained from APSIM simulations across the wheatbelt of Western Australia. Daily meteorological data were obtained from the SILO data for the period of 1967–2017, with a spatial resolution of 0.05° × 0.05° (∼5 km × ∼5 km). Soils were obtained from disaggregated soil polygon maps (Holmes et al., 2015), with a 4

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Fig. 2. The FHL yield for a fast-, mid- and slow-developing cultivars of wheat at Mullewa with a clay (a) and a sand soil (b), Moora with a clay (c) and a sand soil (d), Narrogin with a clay (e) and a sand soil (f), and Lake King with a clay (g) and a sand soil (h). Note: Red, blue and green lines represent the FHL mean yield for fast-, mid- and slow-developing cultivars, respectively. The columns with corresponding colours are the estimated OFPs defined as ≥ 95 % of the peak mean yield from 51 seasons during 1967–2017. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

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Fig. 3. Simulated long-term (1967–2017) mean opening dates (a) and closing dates (b) of the optimal flowering periods (OFPs) for wheat in the southwestern Western Australia.

highest mean FHL yield increased from northeast to southwest, with a range from 2.0–5.0 t ha−1 (Fig. 5b). 3.4. Spatial and temporal patterns of extreme climate factors which determine the optimal flowering period Fig. 6 shows the spatial variability of the long-term average frequency of frost and heat stress events and SMS occurred during sensitive growth stages. The spatial patterns of the OFPs defined in this study (Figs. 3 and 4) resulted from the combined effects of frost, heat and water stresses on wheat yield. Frost occurred more frequently in the central and south-central wheatbelt and was infrequent in the northwest and southern region (Fig. 6). In contrast, heat stress and SMS occurred most frequently in the eastern wheatbelt and the frequency decreased towards the southwest. Early flowering wheat crops experienced more frost in the central and south-central wheatbelt (Fig. 6a); while late flowering crops were more likely to be affected by heat and water stresses in the eastern wheatbelt (Fig. 6b and c). At all selected locations, the average frequency of frost events around flowering decreased, and the average number of heat stress days and SMS increased with later flowering date. Differences in the relative importance of these extremes at different locations are demonstrated in Fig. 7. For example, at the warmer drier site of Mullewa, the average frequency of frost events was less than 1 day for all flowering dates, with the average values of heat stress days and SMS increased with flowering time from 0 to 4 days and 0.4 to 0.6, respectively. While at wetter, colder site Narrogin, the average frequency of frost events decreased with later flowering time from 4 to 0 days, with the average values of heat stress days and SMS increased with flowering time from 0 to 2 days and 0.1 to 0.6, respectively.

Fig. 4. Simulated long-term (1967–2017) mean durations (the difference between close and open dates) of the optimal flowering periods (OFPs) for wheat in the southwestern Western Australia.

3.3. The spatial pattern of the optimal flowering periods The fast-developing cultivar was used in the simulations of flowering time and yield of wheat to determine the spatial patterns in OFPs across the wheatbelt of WA. This fast-developing cultivar represents cultivars currently sown across approximately 80 % of the wheat growing area in WA. Both open and close dates of the OFPs showed large spatial variation across the wheatbelt (Fig. 3), as a result of the spatial variation in climate (temperature and rainfall). The open dates of the OFPs ranged from 23 Jul in the northeast to 8 Oct in the southwest, with a difference of 77 days (Fig. 3a). Generally, the early open dates corresponded to the early close dates, the latter of which ranged from 23 Aug in the northeast wheatbelt to 21 Oct in the southwest, with a difference of 59 days (Fig. 3b). The duration of the OFP varied from 11 to 32 days (Fig. 4), ranging from 11 to 18 days in the west and east of the central wheatbelt, with the range of 18–25 days in most of the rest of the wheatbelt (Fig. 4). Flowering dates corresponding to the peak mean FHL yield ranged from 9 Aug in the northeast to 7 Oct in the southwest (Fig. 5a). The

3.5. Targeting the optimal flowering period via sowing time and cultivar choice The sowing dates required to achieve these OFP’s are determined by the choice of cultivar. Using a typical fast-developing cultivar the sowing dates required to achieve flowering within the OFP were earlier in the northeast and later in the southwest of the wheatbelt (Figure S1). The opening dates of the optimal sowing periods for this cultivar varied from 23 Apr in the northeast to 11 Jun in the southwest, and closing dates were from 11 May in the northeast to 9 Jul in the southwest. While there is a strong interaction between sowing date and cultivar to achieve the OFPs. Fig. 8 shows the optimal sowing dates for fast-, mid6

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Fig. 5. Simulated long-term (1967–2017) mean optimal flowering date of wheat (a) and the associated peak mean frost and heat limited yield (b) for wheat in the southwestern Western Australia.

and slow-developing wheat cultivars to flower within the OFPs at above selected four locations. For a given cultivar, the optimal sowing period varied across locations. For a fast-developing cultivar, the estimated optimal sowing period at Mullewa was between 1 and 20 May, while at Narrogin it was from 13 to 27 May. To achieve the OFP, a fast-developing cultivar should be sown later than slow-developing cultivar as a result of the interactive effect of sowing dates and cultivars on wheat phenological development. For example, at Moora the optimal sowing periods were 4–20 May for the fast-developing cultivar and 18 Apr–8 May for the slow- developing cultivar.

identification of the OFPs of wheat for individual sites (Flohr et al., 2017; Luo et al., 2018) into a large spatial scale, with the feature that multiple stress factors (frost, heat and drought) and their combined effects on wheat yield were considered. Identifying spatial pattern of the OFPs for wheat in the southwest Australia is important as it is an important wheat producing region globally, and this framework can be applied anywhere in the world. These results allow the farmers and agronomists to visualise the OFPs for their specific paddocks, which are of more practical value in the development of crop management plans across a large agricultural region included in this study. The OFPs varied spatially across the wheatbelt, with the opening dates varying from 23 Jul in the northeast to 30 Sep in the southwest and closing dates varying between 23 Aug and 21 Oct from northeast to southwest (Fig. 3). The OFPs did exhibit a clear spatial pattern across the wheatbelt of Western Australia. The opening and closing dates of the OFPs estimated in this study were 2 Aug–11 Sep for most northern part, 12 Aug–21 Sep for the most central part and 22 Aug–11 Oct for the most southern part of the wheatbelt. Anderson and Smith (1990) reported that wheat OFPs varied from 7 to 29 Sep among the 5 field experimental sites over the 3 seasons in the central part of the wheatbelt. While our estimated long-term average OFPs were from 16 Aug to 22 Sep at the five corresponding experimental areas. Shackley and Anderson (1995) found that the OFPs of wheat were 17 Sep–7 Oct among the 3 experimental locations in the medium rainfall zone of the southern wheatbelt and 3–23 Sep at 1 location in the low rainfall zone. The estimated OFPs varied between 25 Aug–24 Sep at corresponding three experimental areas in the medium rainfall zone of the southern wheatbelt and between 22 Aug–14 Sep at the corresponding experimental area in the low rainfall zone. The pattern of results of these field studies conducted in the wheatbelt are mostly consistent with our simulations. However, the OFPs identified by our simulations tended to be earlier than field studies. This difference may have occurred for a number of reasons. Our analysis is the average of multiple seasons, whereas the experiments of Anderson and Smith (1990) and Shackley and Anderson (1995) were based on fewer seasons. Our analysis included the years after 1990 that showed lower than average rainfall and higher than average temperatures than the years before 1990, both of which would led to an earlier shift in the OFPs (Flohr et al., 2017). Furthermore, the analysis of Anderson and Smith (1990) and Shackley and Anderson (1995) used a fixed duration of the OFP by defining the optimum flowering date and then taking 10 days either side of this as the opening and closing dates of the OFP, which was different from the

4. Discussion and conclusions 4.1. The role of cultivars in determining the optimal flowering period Based on the simulated wheat yields and flowering dates, the role of cultivars in determination of the OFP has been identified (Fig. 2). Simulations from these locations and cultivars indicate that the OFP of wheat is largely a function of climate (temperature, radiation, water availability and frost and heat risk), with only minor differences in the OFPs for different cultivar duration types and soil type. This result supports the work of Flohr et al. (2017) for wheat and Lilley et al. (2019) for canola, who reported that the rate of phenological development of a cultivar and soil type only had minor effects on the OFP. Since the OFP is largely related to location-specific climate, rather than cultivars and soil type, farmers and agronomists can match sowing time with cultivar to target the OFPs identified. A simple rule of thumb is that a fast-developing cultivar sown late or a slow-developing cultivar sown early can be used in any environment to achieve the OFPs. Moreover, progress in quantitative modelling of the relationship between the OFPs and cultivars will likely reveal areas where research on breeding can produce the highest payoff in terms of adaptation of wheat to different climatic regions. 4.2. Spatial variation of the optimal flowering periods This study characterized the spatial patterns of wheat OFPs across south-west Western Australia determined by the co-occurrence of frost, heat and water stresses, using a simulation approach. By combining the crop model with climate data at a fine scale (∼5 km × ∼5 km) over a long time period (1967 − 2017) and fine-scale soil data (∼90 m × ∼90 m), we have extended previous research that focused on 7

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experiments often lack the extent of regions and seasons required to capture their variability (Singh et al., 2006). The spatial patterns in opening and closing of the OFPs indicate the spatial variations in climate and soil types over the wheatbelt. The earlier opening of the OFPs estimated for the northeast (warmer and drier) wheatbelt reflects the more important effects of heat and terminal drought, compared to less frequency of frost even though it may also play an important role (Fig. 6a–c). A lower frequency of frost events at flowering stage in a warmer climate was reported by Wang et al. (2015); Zheng et al. (2012) and Luo et al. (2018). This was also reported in the locations with low rainfall climate (Anderson and Smith, 1990; Shackley and Anderson, 1995). In contrast, the later opening of the OFPs in the southwestern wheatbelt region highlighted the more important effects of frost risk (Fig. 6a–c). These results provide support for our hypothesis that the OFPs opened and closed earlier in the northeast than southwest. This means farmers in the northern wheatbelt should aim to have crops flower earlier than those in the southern wheatbelt either by sowing earlier (depending on the timing of opening rains) or using fast-developing cultivars. Although the duration (the difference between close and open dates) of the OFPs also showed spatial variation, the majority of the wheatbelt had durations that were 11–25 days, with slightly shorter durations in the central region compared to those in northern and southern wheatbelt. The relatively narrow duration difference could be because wheat yield for any location was constrained by either frost or heat and/or drought (Fig. 2). This confirms that the OFP for a given environment is determined by a trade-off between the stresses of frost, heat, drought and radiation, rather than individual stress factor (Flohr et al., 2017; Lilley et al., 2019). Consequently, our other hypothesis that duration of the OFP would be longer in the southwest, due to reduced water stress, was rejected. The central wheatbelt had an OFP of shorter duration compared to the northern and southern regions because it was subject to more than one major stress (frost, heat and drought). In contrast the northern wheatbelt had a wider OFP because there was less frost incidence which reduced the penalty for flowering too early; and the southern wheatbelt had a wider OFP because it had higher rainfall and therefore less terminal water stress which reduced the penalty for flowering too late. There was relatively little change in the duration of the OFP from west (high rainfall zone) to east (low-medium rainfall zone) across the wheatbelt. However, the same duration of OFP would affect farm management differently between the east and west parts of the wheatbelt. For economic reasons farms in low- and medium-rainfall zone usually have larger areas of crop compared to those in high rainfall zone (Fletcher et al., 2017). These take a longer time sow to and thus it is more difficult to ensure that all crops flower within the OFP. Added to this is the variability in sowing opportunities due to season-toseason variability in the timing of opening rains. Thus, farms in the eastern wheatbelt face a considerable management challenge to match sowing date and cultivar duration in that they have the largest area of crop to sow, variable sowing opportunities and a narrow OFP. 4.3. Targeting the optimal flowering period by combining sowing date and cultivar In a Mediterranean type environments such as the WA wheatbelt, the correct choice of sowing date and cultivar are critical determinants of yield (Bassu et al., 2009). In this environment a short-duration cultivar sown early develops too quickly in the warm temperatures and flowers early so there is little time for the crop to accumulate above ground-biomass and thus the potential yield is low (Anderson and Smith, 1990). If the time from sowing to flowering were longer, wheat could potentially have a higher biomass production and consequently greater yield (Fischer, 1985). It has been reported that a slow-developing cultivar sown early would achieve higher yield (Shackley and Anderson, 1995; Ishag et al., 1998; Bassu et al., 2009). This was further

Fig. 6. Spatial distribution of the long-term (1967–2017) mean frequency of frost events (a), frequency of heat events (b) and mean soil moisture stress (c) during sensitive stages.

method used in our simulation. Both simulations and field results showed a ubiquitous presence of OFP-by-environment interaction. The modelling approach of determining the OFPs is more powerful as it allows identification of them under various sub-environments. Field 8

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Fig. 7. Temporal characterisation of the frequency of frost and heat events, and soil moisture stress during sensitive growth stages of wheat against flowering date at four locations: Mullewa (a), Moora (b), Narrogin (c) and Lake King (d). The columns represent the optimal flowering periods (OFPs) at the four locations.

should be noted that within a given season there is still a risk that a single frost or heat event may reduce yield. Therefore, farmers may aim to have a spread of flowering within this optimum period. This could either be achieved at the farm or the field level. To some degree a spread of flowering will occur at the farm level due to there being multiple sequential sowing dates (Fletcher et al., 2017), farmers can adjust cultivar as sowing progresses, although in reality they are likely to have a relatively small number of cultivars available. There will also be subtle microclimate between fields on a farm that lead to differences in flowering date. At the field level farmers may mix cultivars to extend the flowering period thereby spreading the risk of a single one-off frost or heat event (Fletcher et al., 2019).

confirmed in this study (Fig. 8). In addition, there is an increased frost stress risk during early grain-filling for crops flowering too early and hence potential yield penalty (Farre et al., 2018). For a slow-developing cultivar sown too late, the shortened period from sowing to flowering and greater degree of water stress from floral initiation to maturity reduce the biomass accumulation and thus decrease the yield potential relative to a slow-developing cultivar sown early (Del Moral et al., 2003; Flohr et al., 2017). For a given location, to ensure a wheat crop flowers within the OFP to maximize yield, sowing date and cultivar phenology type should be appropriately matched. The combination of potential sowing dates and cultivars of various duration generates a range of flowering dates for a given environment. Changes in sowing date can alter the duration of some developmental stages, to different degrees depending on cultivars (Angus et al., 1981; Savin and Nicolas, 1996). To achieve the OFP, cultivars need to be sown at appropriate time to fine-tune the crop life cycle for wheat flowering at appropriate time, which avoids losses from water and temperature stress determines sowing timing for cultivars (Shackley and Anderson, 1995). The interaction of cultivar duration and sowing date will influence how well a cultivar is adapted to a given location (Zubaidi, 2015). If a fast-developing cultivar is sown, the sowing dates to achieve the OFP ranged from 27 Apr–15 May in a low rainfall site in the northeast wheatbelt and from 10 May–5 Jun in a high rainfall site in the southwestern wheatbelt. When a slow-developing cultivar is used (its flowering date is about two weeks later than a fast-developing cultivar), it needed to be sown about 10 days earlier. However, with the combined effect of sowing date and water and temperature stress risk on yield, slow-developing cultivars, if a sowing opportunity is available, would generally out yield a fast- or mid-developing cultivar. In this research we have defined the OFPs that minimise the combined risk of heat, frost and water stresses across multiple seasons. It

4.4. Implications for future climate change These findings also provide growers insight into managing sowing date and cultivar to maximize yield under changing climate. Regions with a Mediterranean climate are anticipated to become warmer and drier with climate change (Hallett et al., 2018). It is projected that there is an increase in mean temperature of 2.2–2.5 °C and a decrease in annual mean rainfall of -2.5 to -10 % by 2070 in WA (Anwar et al., 2015). It is likely that in a warmer climate crops will experience less frequency of frost (Zheng et al., 2012), and greater frequency of heat and drought damage (Lobell et al., 2015). Although recent climate change has resulted in increased frost damage in wheat crops (Crimp et al., 2016a, b), the future impact of frost on wheat crops remains uncertain. Nevertheless, reduced rainfall and increased heat events mean that the OFP for wheat at a given location in southwest Australia is likely to occur earlier in future climates. Wang et al. (2015) predicted through simulations that the optimum flowering date for spring wheat would be advanced about 18 days under high emission scenario due to 9

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Fig. 8. The relationship between sowing date and flowering date for three duration type cultivars (fast, mid and slow) and optimal flowering period (OFP, shaded grey area) at four locations: Mullewa (a), Dalwallinu (b), Narrogin (c) and Lake King (d).

mid-developing cultivars combined with late sowing also can achieve the OFPs.

increased temperature reduced frost occurrence and increased heat stress incidence. With potential earlier optimal flowering date, farmers need to be more aware of what the OFPs are for their paddocks. Previously, with traditional sowings and a relatively small range in phenology types available, farmers could get away with sowing the best cultivar in the knowledge that the breeding company had it about right to achieve the OFPs identified for their farms. Under future climate there would be more potential combinations of cultivars and sowing dates, and farmers need to be aware of this to plan well to match sowing date and cultivar choice to achieve the OFPs. If the current cultivar is still used in the future, sowing dates may need to be moved earlier. By sowing earlier than current practice with appropriate cultivars, wheat growers can potentially maintain or increase yields (Hunt et al., 2018, 2019). In conclusion, it is important to identify the spatial patterns of the OFPs in a large environment to choose the right sowing date and cultivar. The maps of the OFPs are a useful tool for knowing the target OFP. The wheat OFP at a given location is determined by the trade-off between temperature (frost, heat) and water stress (drought) during sensitive growth stages. Our simulations suggest that the OFPs varied spatially across the wheatbelt due to the variation in climate. The estimated opening dates of the OFPs ranged from 23 Jul in the northeast of the wheatbelt to 30 Sep in the southwest, with closing dates varying between 23 Aug and 21 Oct from northeast to southwest. The OFPs also varied in size by region, which were 2 Aug–11 Sep for the northern part, 12 Aug–21 Sep for the central part and 22 Aug–11 Oct for the southern part of the wheatbelt of Western Australia. The majority of the wheatbelt showed a relatively uniform duration of the OFPs that ranged from 11 to 25 days. As far as sowing opportunities would be available, slow-developing cultivars combined with early sowing would be best option to target the OFPs and maximize yield, although the fast- and

CRediT authorship contribution statement Chao Chen: Conceptualization, Methodology, Writing - original draft. Andrew L. Fletcher: Conceptualization, Investigation, Writing review & editing. Noboru Ota: Methodology, Visualization. Bonnie M. Flohr: Writing - review & editing. Julianne M. Lilley: Writing - review & editing. Roger A. Lawes: Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This research was funded by the Grains Research and Development Corporation through the project CSA00056. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.fcr.2019.107710. References ABARES, 2018. Agricultural Commodity Statistics. Available online from:. Department of Agricultural Fisheries and Forestry, Australian Government, Canberra, Australia.

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