Predicting panicle initiation timing in rice grown using water efficient systems

Field Crops Research 239 (2019) 159–164

Contents lists available at ScienceDirect

Field Crops Research journal homepage: www.elsevier.com/locate/fcr

Predicting panicle initiation timing in rice grown using water efficient systems

T

⁎

Rebecca Darbyshirea, , Emma Creanb, Tina Dunna, Brian Dunna a b

New South Wales Department of Primary Industries, Australia Australian National University, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Rice Delayed permanent water Phenology Degree day model Thermal time

Management strategies that improve water efficiency in water-limited rice systems are needed for sustainable production. In southeast Australia growers are increasing implementing drill seeding and also delayed permanent water (DPW) irrigation practice to improve water productivity. This change in timing of permanent water application has a large influence on crop phenology which impacts the timing of crop management practices. Two types of phenological models were assessed to predict panicle initiation (PI) timing in fields managed using drill sowing and DPW. A single-stage model was contrasted with a two-stage efficiency model. The single-stage model assumed temperature across the planting to PI period equally contributes to PI timing. The two-stage efficiency model allowed for differential temperature efficiencies between the pre (aerobic) and post (anaerobic) permanent water periods. Four temperature indices, one growing degree day and three parameterisations of degree day (DD) were tested. Observations of PI from seven seasons and seven locations were used to parameterise (n = 55) and validate (n = 7) the models. The best model was for the two-stage efficiency approach using the original DD parameters with RMSE of 3.8 and 4.4 days for the parameterising and validating data, respectively. The methodology outlined can be used for other varieties, physiological stages and water management strategies to develop models to better predict phenology in rice systems managed with DPW.

1. Introduction Lower irrigation water security and improving nutrient management has led to wide international interest in increasing water use efficiency in rice systems. For example research has been conducted in China (Liang et al., 2013), Philippines (Samoy-Pascual et al., 2019), USA (Linquist et al., 2015) and Uganda (Awio et al., 2015). In a review of 56 studies Carrijo et al. (2017) found that use of some water saving strategies could reduce water use by over 20% with minimal influence on yield. Further benefits of water saving strategies lie in the improvement of environmental credentials of rice production. This includes reduced nutrient run-off (Liang et al., 2013) and reduced greenhouse emissions (Li, 2012; Linquist et al., 2015; Samoy-Pascual et al., 2019). Lowering emissions from rice production is likely to become more important as regions and countries explore and implement policies to mitigate anthropogenic climate change in agriculture (e.g. Ministry for the Environment, 2019). Rice cultivation in Australia is concentrated in the southeast corner of the country and relies on irrigation water. The Murray-Darling river system supplies agricultural irrigation water to this region. River

⁎

inflows are highly variable with historical flows into the southern part of the Murray-Darling system ranging from over 25,000 G L per year to less than 2500 G L per year (MDBA, 2014). This variability has led to variable water availability for irrigated rice which has constrained growing opportunities (Gaydon et al., 2012). Lower rainfall as a result of climate change in water storage regions will likely further compound irrigation water pressures. In response, increasing water use efficiency has been the focus of research in Australia (Dunn and Gaydon, 2011; Humphreys et al., 2006) as well as in many rice growing countries (Carrijo et al., 2017). Australian grower uptake of direct drill sowing in combination with delayed permanent water (DPW) has increased in recent years. In the 2000 crop year 7% of the rice crop grown in south-eastern Australia was drill sown, this percentage has been gradually increasing with 35% of the crop drill sown in 2016 and 67% in 2019 (pers. comm., Mark Grout, SunRice 2019). Conventional drill seeding involves direct drill seeding followed by 2–3 'flush’ irrigations to take plant growth to the 3–4 leaf stage when permanent water is applied to the field. With DPW the period of ‘flush’ irrigation is extended for a variable period (often depending on weed control) with permanent water applied not later

Corresponding author. E-mail address: [email protected] (R. Darbyshire).

https://doi.org/10.1016/j.fcr.2019.05.018 Received 28 November 2018; Received in revised form 23 May 2019; Accepted 26 May 2019 Available online 03 June 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

Field Crops Research 239 (2019) 159–164

R. Darbyshire, et al.

prior to permanent water (PW) was managed at the sites using current commercial practice which recommends limited moisture stress of the rice crop (Dunn, 2018). Panicle initiation was determined by field observations and laboratory confirmation. A sample of ten plants was collected from random locations within rice variety by nitrogen experiments at commercial nitrogen rates. In the laboratory the main tiller was removed from each plant and dissected. Panicle initiation was defined as when three of the ten main tillers have a panicle one to three mm long with a detailed description in Dunn and Dunn (2018). This stage is also referred to as ‘jointing stage’ (Hardke, 2018). The resulting PI date for each site was the average of two replicates. In total 62 unique PI observations were taken. Paired observations of Reiziq and Sherpa (22 data points) were evaluated to assess whether data from these two varieties could be pooled for modelling processes. Statistical analyses (t-test, f-test, R2) found non-significant difference (5% significance level) between the group means and variance and a high correlation (R2 = 0.98). From this process it was determined that the data could be pooled across the two varieties for modelling purposes. Sowing day-of-year and PW day-of-year varied between sites and years with various combinations of total days pre and post PW (Table 2). The dataset was separated in parameterising (n = 55) and validating data (n = 7). The validating data was not selected randomly. Specific points were chosen to provide representation across sites, years and number of days pre PW. This was conducted to ensure the validating dataset contained variation in pre PW day range and climate conditions to test the robustness of the model.

than 10–14 days prior to panicle initiation (PI) (Dunn, 2018). DPW has been shown to reduce water use by 8–18% (Thompson and Griffin, 2006) and 10–22% (Dunn and Gaydon, 2011). Yield penalties using DPW management have been found to be approximately 10% (Dunn and Gaydon, 2011), however they noted that the increase in water productivity (up to 17%) led to an overall economic benefit of implementing DPW. Under DPW management rice phenology is influenced. For instance, Dunn and Gaydon (2011) found that microspore was delayed by 8–12 days for crops grown under DPW compared with traditional ponding and Heenan and Thompson (1984) found DPW delayed flowering by 12 days. Increasing the time between flush irrigations increases crop moisture stress, which can further delay crop development (Dunn and Gaydon, 2011). This modification to phenology is particularly important to consider at PI. PI is an efficient time to apply nitrogen (Dunn et al., 2016; Humphreys et al., 2006) and, in Australia, PI provides sufficient lead time to increase water depth to protect the microspore stage from potential cold damage. Phenological models provide a method to predict PI timing based on temperature conditions. With prediction of PI rice growers are better able to manage nitrogen and water levels to improve production outcomes. This has been recognised by the Australian rice industry with an online tool developed to assist growers to predict PI (Rice Extension, 2018). The PI predictor tool was based on the calculations in the MaNage Rice model (Angus et al., 1996) which did not include DPW management. Phenological modelling for agricultural crops is an established field of research (Hudson and Keatley, 2009; Zhao et al., 2013). This includes for rice with Oryza2000 (Bouman and Van Laar, 2006) and RiceClock model (Gao et al., 1992) two examples. Recently Sharifi et al. (2017) evaluated several rice phenological phases using the thermal time model degree days (DD) with cardinal temperature values optimised in the modelling processes. They modelled the planting to PI phase, amongst various other phases, and found that modified parameters of the original DD model (Slaton et al., 1993) performed best. Their optimised models had root mean square error (RMSE) of 2.7 and 3.3 days for the planting to PI period. This research aims to model PI timing in systems that utilise drill seeding and DPW management and represents the first analysis of its kind. The timing of permanent water is a management decision and represents the transition from an aerobic to an anaerobic environment. To evaluate PI timing, two phenological models were assessed for predictive capability. Firstly, a single-stage model was assessed, which is the common approach and assumes temperature across the whole planting to PI period contributes equally to PI timing. Secondly, a twostage efficiency model was assessed, which separates the pre- and postpermanent water periods and allows thermal time accumulation during these periods to contribute differentially to PI timing. The two-stage model allows for different efficiencies in the contribution of temperature to reaching PI pre- and post-permanent water. The single and twostage models were tested using four temperature indices; one growing degree days (GDD) parameterisation using base temperature of 10 °C and three parameterisations of DD, the original parameters (Slaton et al., 1993) and two sets of parameters derived by Sharifi et al. (2017).

2.2. Temperature data Daily maximum and minimum temperature data was recorded at each site and where available, on site weather data was used. Temperature data was collected using ‘Tinytag Plus 2’ data loggers (TPG-4510) with a built in temperature sensor mounted in a plastic Stevenson type screen (ACS-5050). The screen was located in the rice field at a height of 1.5 m above the ground. Missing values from the weather stations were interpolated to construct a continuous time series for each site. A combination of data sources were used for interpolation with, where available data from a close by Australian Bureau of Meteorology station data used and in absence of this information interpolated data from the Scientific Information for Land Owners dataset (Jeffrey et al., 2001) was used. Details of the temperature data for each site are summarised in Table 3. 2.3. Phenology models Two phenology models were tested, a single-stage (Equ 1) and a two-stage efficiency (Equ 2) model. The single-stage model assumes that the contribution of temperature to reaching PI is fixed pre and post PW. The two-stage model allows for different efficiencies in the contribution of temperature to reaching PI pre and post PW. PI

Thres = e∑ IND

(1)

sow PW

2. Methods

PI

Thres = e1 ∑ IND + e2 ∑ IND sow

2.1. Panicle initiation data and site location

PW

(2)

Where Thres is the threshold summation required to reach PI, IND is the temperature index, sow is day-of-year of sowing, PI is panicle initiation day-of-year, PW is permanent water day-of-year and e, e1 and e2 are the coefficients for sow to PI, sow to PW, and PW to PI, respectively. Both of these models were evaluated using two temperature indices, growing degree days (GDD) and degree days (DD) (Slaton et al., 1993). GDD and DD use the same thermal time structure (Equ 3) with the DD model imposing temperature constraints on daily maximum and

Panicle initiation was observed for two commercial semi-dwarf rice varieties; Reiziq and Sherpa (Troldahl et al., 2018). Data was collected for seven seasons (2011/12 to 2017/18) across seven sites in southeastern Australia, the major irrigated rice growing region in Australia (Fig. 1). The average climate conditions at these sites is summarised in Table 1. The frequency of flush irrigation applications during the period 160

Field Crops Research 239 (2019) 159–164

R. Darbyshire, et al.

Fig. 1. Location of the seven panicle initiation observation sites. Note two sites (Yanco 1 and Yanco 2) were both located in close proximity at Yanco.

2.4. Model evaluation

minimum temperatures (Equ 4)

max ⎧ ⎡ ⎨ ⎩⎣

(Tmax + Tmin ) − Tb⎤; 2 ⎦

0⎫ ⎬ ⎭

The objective of the research was to build a predictive model. As such, Equ 1 and Equ 2 were used to solve for PI day-of-year with the parameters Thres and e (Equ 1) and Thres, e1 and e2 (Equ 2) optimised using the parameterising data. In this process various parameter values were tested (Table 5) with 1,890 and 37,800 combinations tested for the single-stage and two-stage models, respectively. For each parameter combination Root Mean Square Error (RMSE) was calculated between the predicted and observed PI day-of-year. From this the best performing model based on RMSE minimisation for the single-stage and two-stage models and for each of the temperature indices were selected (i.e. four indices tested for the single and twostage models each). These models were then validated with the validating data with model performance assessed using RMSE. RMSE was compared with the standard deviation of the observations, which represents uncertainty in the observations (Gaydon et al., 2017). RMSE

(3)

Tmin = min {Tmin; Tl } Tmax = min {Tmax ; Topt }

(4)

Where Tmax and Tmin are daily maximum and minimum temperature (°C) and Tb is the base temperature. Tb was set to 10 °C for the GDD index. Tl and Topt represent the lower and optimum thresholds, respectively, for the DD model. Three sets of parameters were tested for the DD temperature index (Table 4). These were the original DD parameters (DD0) (Slaton et al., 1993), those found to be optimal for estimating planting to PI (DD1) (Sharifi et al., 2017) and those found optimal across growth phases (DD2) (Sharifi et al., 2017).

Table 1 Site summary of climate conditions. Tmax and Tmin is the mean maximum and minimum temperatures (°C) and Rain is the mean rainfall (mm). Spring is September to November, Summer is December to February, Autumn is March to May and Winter is June to August. Site

Coleambally Finley Griffith Jerilderie Wakool Yanco

Spring

Summer

Autumn

Winter

Tmin (°C)

Tmax (°C)

Rain (mm)

Tmin (°C)

Tmax (°C)

Rain (mm)

Tmin (°C)

Tmax (°C)

Rain (mm)

Tmax (°C)

Tmin (°C)

Rain (mm)

9.3 8.7 9.4 9.3 9.1 9.5

23.6 22.7 23.8 23.6 23.3 23.7

107 116 105 102 102 112

16.4 15.4 16.6 16.4 15.5 16.8

31.7 31.1 31.7 31.8 31.4 31.9

99 98 89 92 88 97

10.3 9.9 10.2 10.3 10.1 10.6

23.7 23.1 23.7 23.6 23.4 23.8

95 102 95 91 91 108

4.0 3.9 3.9 4.0 4.2 4.2

15.2 14.7 15.5 15.2 15.2 15.2

105 112 105 104 103 115

161

Field Crops Research 239 (2019) 159–164

R. Darbyshire, et al.

smaller than the standard deviation of the observations is indicative of robust model performance.

Table 2 Description of the parameterising and validating data. Year(s) is the year(s) of sowing. PW is permanent water and n is the sample size. Site

Coleambally Finely Griffith Jerilderie Wakool Yanco1

Variety

Year(s)

Parameterising data Reiziq 2016 Reiziq 2016 Sherpa, 2015 Reiziq Sherpa, 2015Reiziq 2017 Sherpa 2017 Sherpa, 2011Reiziq 2017 Reiziq 2014

Yanco2 TOTAL Validating data Coleambally Sherpa Jerilderie Reiziq Wakool Reiziq Yanco1 Reiziq, Sherpa Yanco2 Sherpa TOTAL

2016 2015 2017 20112013 2014

Pre PW range (days)

Post PW range (days)

3. Results

n

59 63 26

29 27 36-37

1 1 2

74-85

18-37

7

89 54-103

26 3-46

1 42

71

36

1 55

30 67 63 30-85

30 20 26 9-40

1 1 1 3

35

31

1 7

The top combinations of parameters, based on RMSE, were tabulated for the single-stage and two-stage phenology models and for each temperature index (GDD, DD0, DD1, DD2) (Table 6). All parameterised models RMSEp were better than the standard deviation of the observations (10 days). The results show that RMSEp of the two-stage model was 2.8–3.5 days better than the single-stage model across the four temperature indices used. For the two-stage model results, there was little difference in model performance between temperature indices with less than 0.3 days and 0.5 days difference between RMSEp and RMSEv, respectively. The overall best performing model was the two-stage model using DD0 temperature index with Thres of 250 DD, e1 of 0.2 and e2 of 0.4. This model recorded a RMSE of 3.8 days for the parameterising data and 4.4 days for the validating data. The modelled and observed PI data are plotted in Fig. 2 to illustrate the model fit. 4. Discussion The best phenology model was a two-stage efficiency model which represented two distinct environmental growing conditions prior to PI (aerobic and anaerobic). This model predicted PI with RMSEp less than 4.1 days which compared favourably with the standard deviation of the observations (10 days) which is an indicator of model robustness (Gaydon et al., 2017). The best single-stage models, which did not recognise different environmental conditions prior to PI, recorded RMSEp approximately 40% higher than the best two-stage models (> 6.6 days). In addition to contrasting phenology model structure, four temperature indices were assessed. All indices performed similarly in the two-stage efficiency modelling with only a 0.3 day difference in RMSEp across all four options (Table 6). Of these the DD1 temperature index performed the least well, which is somewhat unexpected as this DD model was parameterised for the sowing to PI phase (Sharifi et al., 2017). However, as noted the differences were small. The overall best model fit was for the DD0 temperature index which used the original parameters (Slaton et al., 1993) (Table 6 and Fig. 2). This model recorded RMSE for the parameterising and validating data of 3.8 and 4.4 days, respectively. This compares well with findings by Sharifi et al. (2017) who reported RMSE of the sowing to PI phase of rice grown in ponded water in California between 2.0 and 3.7 days. Optimisation of the temperature indices parameters, such as conducted by Sharifi et al. (2017) and recommended by others (Awan et al., 2014; van Oort et al., 2011v), may further improve the modelled results. However, the RMSE found are likely to be within the range of possible model performance for this dataset given some observer error is likely (i.e. variation of phenological stages of plants within the plot and sampling was not conducted every day) and some temperature data was sourced off-site (Table 3). The precision of the overall best model, approximately 4 days, is sufficient to provide rice growers with useful information to implement management strategies. This model can be incorporated into the industry supported ‘PI Predictor’ decision support tool (Rice Extension, 2018) which provides growers with forecasted PI date based on variety and management choice. Upgrading this service with the two-stage efficiency model will improve predictions for growers who planted Reiziq and Sherpa and use DPW which will improve nitrogen efficiency and ensure high water is in place prior to microspore protecting plants and improving yields. The best two-stage efficiency models all were fit with smaller e1 than e2 efficiency parameters. e1 tended to be approximately half the value of e2 (Table 6). This finding indicates that temperatures received

Table 3 Description of temperature data utilised for each site. BoM is bureau of meteorology station. Site

On site

BoM

SILO

Coleambally Finely Griffith* Jerilderie Wakool Yanco1 Yanco2

85% 100% 50% 85% – 99% 99%

– – 50% 15% 100% 1% 1%

15% – – – – – –

* The Griffith BoM site is 18 km west of the field site and for data common between the field sensor and the BoM data there was less than 2 °C difference. This data was only used for two data points. Table 4 Parameterisations of the three DD index assessed. Index name

Topt (°C)

Tl (°C)

Tb (°C)

DD0 DD1 DD2

34.4 27.7 32.7

21.1 14.2 13.2

10 9.9 11.5

Table 5 Parameter values tested in model optimisation. DD0, DD1, DD2 are DD indices with different parameters (Table 4). Single-stage Parameter Thres e Two-stage Parameter Thres e1 e2

Tested values GDD (100 to 1500 in 50GDD increments) DD0, DD1, DD2 (100 to 1500 in 50GDD increments) 0 to 2 in 0.1 increments Tested values GDD (100 to 1500 in 50GGD increments) DD0, DD1, DD2 (100 to 1500 in 50DD increments) 0 to 2 in 0.1 increments 0.1 to 2 in 0.1 increments

162

Field Crops Research 239 (2019) 159–164

R. Darbyshire, et al.

Table 6 Parameter combinations of top models based on RMSEp of parameterising data for single-stage and two-stage models and all temperature indices. RMSEv is the RMSE of the independent validation data. Parameters order was Thres; e for single-stage model (Equ 1) and Thres; e1; e2 for two-stage model (Equ 2). Single-stage

Two-stage

Temperature Index

Parameters

RMSEp (days)

RMSEv (days)

Parameters

RMSEp (days)

RMSEv (days)

GDD DD0 DD1 DD2

800; 0.9 1300; 1.5 1200; 1.7 1250; 1.9

6.6 6.6 7.6 7.0

9.4 9.3 10.1 9.6

1300; 1.1; 1.9 250; 0.2; 0.4 950; 1.0; 1.8 600; 0.7; 1.2

3.8 3.8 4.1 3.8

4.9 4.4 4.5 4.5

that explored by Sharifi et al. (2018) may further improve the results here. In this context air temperature would need to be used up until permanent water was applied. Lack of water temperature measurement by growers is a drawback using water temperature for phenology models aimed to assist seasonal management decisions. Simulations of water temperature (Confalonieri et al., 2005; Kuwagata et al., 2008) may overcome this hindrance, although these simulated water temperatures require other input data (e.g. solar radiation and wind speed) which are also not commonly logged. The varieties assessed here (Sherpa and Reiziqi) are not photoperiod sensitive as they do not have the major gene that makes rice flower when days get shorter (pers comms. Ben Ovenden, rice breeder NSW DPI). For other varieties, including photoperiod may improve model results. Awan et al. (2014) found phenophases for four aerobic rice varieties to be sensitive to photoperiod. The data in this study included different periods of time between first flush and permanent water. This could translate to different levels of water stress in the plants which was not explicitly included in the model and could influence PI timing. Noting that the data was collected under commercial conditions whereby water stress is recommended to be minimised (Dunn, 2018). Similarly, differences in soil nitrogen between sites, years and timing of permanent water may also influence phenology timing. Testing of all these factors on PI timing for new management systems may further improve model performance. As anthropogenic climate change continues it is likely that irrigation water availability will become less reliable as rainfall decreases across southern Australia (CSIRO and BoM, 2015). This highlights the need for more water efficient rice systems. Rice grown using DPW management may provide an option for profitable rice production in future climates. This is also relevant internationally with similar water saving practices investigated for Italy (Miniotti et al., 2016), Bangladesh (Md. Anisuzzaman et al., 2015), Nepal (Howell et al., 2015), USA (Linquist et al., 2015) and many other countries as summarised by Carrijo et al. (2017). As more water efficient management strategies become commonplace assessments of phenological implications are required to support growers in making profitable and sustainable decisions. This study provided a broad methodology to build predictive models for key physiological phases in delayed permanent water systems. With greater research, the approach could be applied and tested for different physiological stages, varieties and modified for alternate delayed water strategies.

Fig. 2. The statistically best model fit. This is the two-stage model with parameters of Thres = 250 DD0; e1 = 0.2; e2 = 0.4 using DD0 temperature.

prior to permanent water in the aerobic phase are half as efficient in stimulating PI as temperatures received during the anaerobic (permanent water) phase. Rice is a semi-aquatic plant that has adapted to grow in water and the water layer has a smoothing effect on temperature mitigating diurnal temperatures fluctuations. The combination of consistent temperature and lack of potential water stress, which delays development (Dunn and Gaydon, 2011), are expected to be responsible for the faster growth during the anaerobic phase. To further the findings of this study testing of the model outside of Australia is required to assess the general applicability of the model for Reiziq and Sherpa. Data from an international temperature zone would provide further validation of these findings. Investigation of DPW and PI phenology in tropical regions would highlight whether smaller diurnal temperature ranges in tropical areas led to PI delay as observed in the temperate conditions. Further research is also required for other commonly grown varieties to evaluate a range of similar PI models across varieties. Investigation into potential delays to phenology under DPW management on other important physiological stages (flowering, heading) is also required. This study presented the first analysis of PI timing for DPW systems. These findings need further research prior to incorporation into process-based models, such as ORYZA2000 (Bouman et al., 2001). Some limitations include use of air temperature and understanding of water or nitrogen stress on PI timing. Air temperature alone was used for the assessments. After permanent water was applied the plant developed to the PI phase under water with water temperatures, rather than air temperatures, likely driving phenological timing. Sharifi et al. (2018) found for flood irrigated rice that using water temperature to predict PI timing improved RMSE by approximately 2.5 days compared with air temperature. A combined air and water temperature model, such as

Acknowledgements Funding for the research was provided by AgriFutures Australia and NSW Department of Primary Industries, Australia. We would like to thank Craig Hodges and Chris Dawe for establishing the field experiments and the two anonymous reviews of this paper who provided a constructive review which notably improved the paper. References Angus, J., Williams, R., Durkin, C., 1996. MANAGE RICE: decision support for tactical crop management. Proceedings of the 2nd Asian Crop Science Conference.

163

Field Crops Research 239 (2019) 159–164

R. Darbyshire, et al.

paddy for agro-environmental research. Agric. For. Meteorol. 148 (11), 1754–1766. https://doi.org/10.1016/j.agrformet.2008.06.011. Li, D., 2012. Effect of water-saving irrigation on CH 4 emissions from rice fields. Adv. Mater. Res. 1950–1958. Liang, X.Q., et al., 2013. Mitigation of nutrient losses via surface runoff from rice cropping systems with alternate wetting and drying irrigation and site-specific nutrient management practices. Environ. Sci. Pollut. Res. - Int. 20 (10), 6980–6991. https:// doi.org/10.1007/s11356-012-1391-1. Linquist, B.A., et al., 2015. Reducing greenhouse gas emissions, water use, and grain arsenic levels in rice systems. Glob. Chang. Biol. 21 (1), 407–417. https://doi.org/10. 1111/gcb.12701. Md. Anisuzzaman, K., Md. Obayedul Hoque, R., Md. Touhiduzzaman, k., Muhammad Aslam, A., 2015. Effect of irrigation water management practices and rice cultivars on methane (CH4) emission and rice productivity. Int. J. Innov. Appl. Stud. 10 (2), 516–534. MDBA, 2014. Surface Water Inflows Timeline. (Accessed 17 September 2018). https:// www.mdba.gov.au/publications/products/surface-water-inflows-timeline. Miniotti, E.F., et al., 2016. Agro-environmental sustainability of different water management practices in temperate rice agro-ecosystems. Agric. Ecosyst. Environ. 222, 235–248. https://doi.org/10.1016/j.agee.2016.02.010. Ministry for the Environment, 2019. Climate Change Response (Zero Carbon) Amendment Bill: Summary, Wellington. https://www.mfe.govt.nz/sites/default/ files/media/Climate%20Change/climate-change-response-zero-carbon-amendmentbill-summary.pdf. Rice Extension, 2018. PI Predictor. (Accessed 17 September 2018). https://pipredictor. riceextension.org.au/. Samoy-Pascual, K., et al., 2019. Is alternate wetting and drying irrigation technique enough to reduce methane emission from a tropical rice paddy? Soil Sci. Plant Nutr. https://doi.org/10.1080/00380768.2019.1579615. Sharifi, H., Hijmans, R.J., Hill, J.E., Linquist, B.A., 2017. Using stage-dependent temperature parameters to improve phenological model prediction accuracy in rice models. Crop Sci. 57 (1), 444–453. https://doi.org/10.2135/cropsci2016.01.0072. Sharifi, H., Hijmans, R.J., Hill, J.E., Linquist, B.A., 2018. Water and air temperature impacts on rice (Oryza sativa) phenology. Paddy Water Environ. 16 (3), 467–476. https://doi.org/10.1007/s10333-018-0640-4. Slaton, N.A., Helms, S., Wells, B., 1993. DD50 Computerized Rice Management Program. Cooperative Extension Service, University of Arkansas, US Department of Agriculture, and county governments cooperating. Thompson, J., Griffin, D., 2006. Delayed flooding of rice–effect on yields and water. IREC Farm. Newslett. 173 (Spring), 52–53. Troldahl, D., Snell, P., Dunn, B., 2018. Rice Variety Guide 2018–19. Primefact 1112. pp 6.. NSW Department of Primary Industries. www.dpi.nsw.gov.au/__data/assets/pdf_ file/0003/401358/rice-variety-guide-2018-19.pdf. van Oort, P.A.J., Zhang, T., de Vries, M.E., Heinemann, A.B., Meinke, H., 2011v. Correlation between temperature and phenology prediction error in rice (Oryza sativa L.). Agric. For. Meteorol. 151 (12), 1545–1555. https://doi.org/10.1016/j. agrformet.2011.06.012. Zhao, M., et al., 2013. Plant phenological modeling and its application in global climate change research: overview and future challenges. Environ. Rev. 21 (1), 1–14. https:// doi.org/10.1139/er-2012-0036.

Awan, M.I., et al., 2014. A two-step approach to quantify photothermal effects on preflowering rice phenology. Field Crops Res. 155, 14–22. https://doi.org/10.1016/j.fcr. 2013.09.027. Awio, T., Bua, B., Karungi, J., 2015. Assessing the effects of water management regimes and rice residue on growth and yield of rice in Uganda. Am. J. Exp. Agric. 7 (2), 141–149. https://doi.org/10.9734/AJEA/2015/15631. Bouman, B., et al., 2001. Oryza2000: Modeling Lowland Rice. IRRI, Philippines. Bouman, B., Van Laar, H., 2006. Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agric. Syst. 87 (3), 249–273. Carrijo, D.R., Lundy, M.E., Linquist, B.A., 2017. Rice yields and water use under alternate wetting and drying irrigation: a meta-analysis. Field Crops Res. 203, 173–180. https://doi.org/10.1016/j.fcr.2016.12.002. Confalonieri, R., Mariani, L., Bocchi, S., 2005. Analysis and modelling of water and near water temperatures in flooded rice (Oryza sativa L.). Ecol. Modell. 183 (2), 269–280. https://doi.org/10.1016/j.ecolmodel.2004.07.031. CSIRO and BoM, 2015. Climate Change in Australia Information for Australia’s Natural Resource Management Regions: Technical Report, Australia. pp 216. . Dunn, B., 2018. Dealying Permanent Water on Drill Sown Rice. Accessed 23 May 2019. https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0007/438955/delayingpermanent-water-on-drill-sown-rice.pdf. Dunn, B., Dunn, T., Orchard, B., 2016. Nitrogen rate and timing effects on growth and yield of drill-sown rice. Crop Pasture Sci. 67 (11), 1149–1157. Dunn, B., Gaydon, D., 2011. Rice growth, yield and water productivity responses to irrigation scheduling prior to the delayed application of continuous flooding in southeast Australia. Agric. Water Manage. 98 (12), 1799–1807. Dunn, T., Dunn, B., 2018. Identifying panicle initiation in rice. Primefact 1278. NSW Department of Primary Industires pp 3. http://www.dpi.nsw.gov.au/__data/assets/ pdf_file/0003/449823/identifying-panicle-initiation-in-rice.pdf. Gao, L., Jin, Z., Huang, Y., Zhang, L., 1992. Rice clock model—a computer model to simulate rice development. Agric. For. Meteorol. 60 (1-2), 1–16. Gaydon, D.S., et al., 2017. Evaluation of the APSIM model in cropping systems of Asia. Field Crops Res. 204, 52–75. https://doi.org/10.1016/j.fcr.2016.12.015. Gaydon, D.S., Meinke, H., Rodriguez, D., 2012. The best farm-level irrigation strategy changes seasonally with fluctuating water availability. Agric. Water Manage. 103, 33–42. https://doi.org/10.1016/j.agwat.2011.10.015. Hardke, J.T. (Ed.), 2018. Rice Production Handbook. University of Arkansas Division of Agriculture, Cooperative Extension Service, Little Rock, Arkansas 214 pp. Heenan, D., Thompson, J., 1984. Growth, grain yield and water use of rice grown under restricted water supply in New South Wales. Aust. J. Exp. Agric. 24 (124), 104–109. Howell, K.R., Shrestha, P., Dodd, I.C., 2015. Alternate wetting and drying irrigation maintained rice yields despite half the irrigation volume, but is currently unlikely to be adopted by smallholder lowland rice farmers in Nepal. Food Energy Secur. 4 (2), 144–157. https://doi.org/10.1002/fes3.58. Hudson, I.L., Keatley, M.R., 2009. Phenological Research: Methods For Environmental and Climate Change Analysis. Springer Science & Business Media. Humphreys, E., et al., 2006. Integration of approaches to increasing water use efficiency in rice-based systems in southeast Australia. Field Crops Res. 97 (1), 19–33. Jeffrey, S.J., Carter, J.O., Moodie, K.B., Beswick, A.R., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Softw. 16 (4), 309–330. https://doi.org/10.1016/S1364-8152(01)00008-1. Kuwagata, T., Hamasaki, T., Watanabe, T., 2008. Modeling water temperature in a rice

164