Improving water productivity in moisture-limited rice-based cropping systems through incorporation of maize and mungbean: A modelling approach

Agricultural Water Management 189 (2017) 111–122

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Improving water productivity in moisture-limited rice-based cropping systems through incorporation of maize and mungbean: A modelling approach R.P.R.K. Amarasingha a , L.D.B. Suriyagoda b,∗ , B. Marambe b , W.M.U.K. Rathnayake c , D.S. Gaydon d , L.W. Galagedara b,e , R. Punyawardena f , G.L.L.P. Silva b , U. Nidumolu d , M. Howden g a

Postgraduate Institute of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka Faculty of Agriculture, University of Peradeniya, Peradeniya, Sri Lanka c Rice Research and Development Institute, Bathalagoda, Ibbagamuwa, Sri Lanka d Agriculture Flagship, Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia e Grenfell Campus-Memorial University of Newfoundland, NL, Canada f Natural Resources Management Centre, Department of Agriculture, Peradeniya, Sri Lanka g Climate Change Institute, Australian National University, Canberra, Australia b

a r t i c l e

i n f o

Article history: Received 6 November 2016 Received in revised form 1 May 2017 Accepted 6 May 2017 Keywords: Apsim Intercropping Irrigation Maize Mungbean Rice Sri Lanka

a b s t r a c t Crop and water productivities of rice-based cropping systems and cropping patterns in the irrigated lowlands of Sri Lanka have not been researched to the degree warranted given their significance as critical food sources. In order to reduce this knowledge gap, we simulated the water requirement for rice, maize, and mungbean under rice-based cropping systems in the Dry Zone of Sri Lanka. We evaluated the best combinations of crops for minimum water usage while reaching higher crop and water productivities. We also assessed the risk of cultivating mungbean as the third season/sandwich crop (i.e. rice-mungbean-rice) in different regions in Sri Lanka. In the simulation modelling exercise, APSIMOryza (rice), APSIM-maize and APSIM-mungbean modules were parameterised and validated for varieties grown widely in Sri Lanka. Moreover, crop productivities and supplementary irrigation requirement were tested under two management scenarios i.e. Scenario 1: irrigate when plant available water content in soil fell below 25% of maximum, and Scenario 2: irrigate at 7-day intervals (current farmer practice). The parameterised, calibrated and validated model estimated the irrigation water requirement (number of pairs of observations (n) = 14, R2 > 0.9, RMSE = 66 mm season−1 ha−1 ), and grain yield of maize (n = 37, R2 > 0.95, RMSE = 353 kg ha−1 ) and mungbean (n = 26, R2 > 0.98, RMSE = 75 kg ha−1 ) with a strong fit in comparison with observed data, across years, cultivating seasons, regions, management conditions and varieties. Simulated water requirement during the cropping season reduced in the order of rice (1180–1520 mm) > maize and mungbean intercrop = maize sole crop (637–672 mm) > mungbean sole crop (345 mm). The water productivity of the system (crop yield per unit water) could be increased by over 65% when maize or mungbean extent was increased. The most efficient crop combinations to maximise net return were diversification of the land extent as (i) 50% to rice and 50% to mungbean sole crops, or (ii) 25%, 25% and 50% to rice, maize and mungbean sole crops, respectively. Under situations where water availability is inadequate for rice, land extent could be cultivated to 50% maize and 50% mungbean as sole crops to ensure the maximum net return per unit irrigation water (115 Sri Lankan Rupees ha−1 mm−1 ). Regions with high rainfall during the preceding rice cultivating season are expected to have minimum risk when incorporating a third season mungbean crop. Moisture loss through evapotranspiration from the third season mungbean crop was similar to that of a fallowed site with weeds. © 2017 Elsevier B.V. All rights reserved.

1. Introduction ∗ Corresponding author. E-mail address: [email protected] (L.D.B. Suriyagoda). http://dx.doi.org/10.1016/j.agwat.2017.05.002 0378-3774/© 2017 Elsevier B.V. All rights reserved.

Increasing food crop production to fulfil the demand of a continuously growing world population has become a great challenge

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and this is further exacerbated by increasing scarcity of and demand for water. At present, water is often inefficiently managed in agriculture (Tuong and Bhuiyan, 1999; Singh et al., 2001; Yao et al., 2012). Rice (Oryza sativa L.) is widely grown and consumed in resource-poor communities in Asia and it is one of the largest water consumers in the world (Gheewala et al., 2014). The Asian region has been identified as an area of high climate risk globally under projected climate change scenarios (IPCC, 2013) further potentially aggravating existing scarcity and heterogeneity in water availability (Liu et al., 2012; IPCC, 2013). Due to these challenges, crop productivity (CP: grain yield per unit land area) and water productivity (WP: grain yield per unit of total water input) must be increased to reach global food security targets (Masikati et al., 2014; Amarasingha et al., 2015a,b). Therefore, it is important to identify innovative cultural and/or agronomic practices in rice-growing ecosystems with the aim of enhancing CP and WP (Dobermann and Witt, 2000; Bouman et al., 2007). Changing non-flooded crops to traditional rice rotations (Cho et al., 2003; Singh et al., 2005), efficient irrigation water management (Bouman and Tuong, 2001; Yadav et al., 2011; Amarasingha et al., 2014, 2015b) and crop genetic improvement (Peng et al., 1999; Sheehy et al., 2000) are some of the suggested practices in this context. Additionally, selection of appropriate crops and/or crop combinations as adaptive agricultural practices could operate to increase the effective cultivable area, and/or conserve rain water for use in drier seasons through irrigation (Amarasingha et al., 2015a,b). Cultivation decisions made by rice farmers in Asia are based on the predicted onset of the rainfall season and expected amount of rainfall (Cassman et al., 1997; Amarasingha et al., 2014, 2015b). In Sri Lanka, there are two major cultivating seasons (based on the rainfall distribution over the country) known as Maha and Yala. Maha is the major rainy season from September to February where rainfall is widely distributed across the country, with substantially higher cumulative seasonal rainfall. Yala is the minor rainy season from March to August with comparatively low cumulative seasonal rainfall along with very high spatial and temporal variability (Figs 1 and S1). During the Maha season, farmers prioritise rice cultivation in lowlands. If the amount and duration of rainfall is not adequate for rice cultivation, supplementary irrigation is provided using stored water from irrigation reservoirs. However, during the Yala season, as most of the areas in the country including the Dry Zone do not receive adequate rainfall, cultivation decisions are based on the amount of water available in the reservoirs for irrigation (Fig. 1). Under such circumstances, farmers and government officials from the Departments of Agriculture, Agrarian Development and Irrigation have to make crucial decisions regarding cultivation. Some of these decisions on water issues are; (i) whether to cultivate rice or not, (ii) the fraction of lowlands that could be cultivated with rice using the available water in reservoirs, (iii) the type(s) and extents of other field crops requiring lesser amounts of water that could be cultivated to maximise CP and WP, and (iv) how the land area under cultivation could be increased. Similar considerations prevail in most of the rice-based cropping systems in other countries in the region (Zeigler and Puckridge, 1995; Jat et al., 2012). Maize (Zea mays L.) is one of the most important field crops grown in Asia (Liu et al., 2012; Gheewala et al., 2014; Wang et al., 2014), and occupies the second highest cultivation extent in Sri Lanka after rice (Ranaweera et al., 2009). Mungbean [Vigna radiata (L.) R. Wilczek] is also one of the relevant pulse crops grown in Asia due to its importance as a source of high-quality protein in the cereal-based diets of many people (Tharanathan and Mahadevamma, 2003; Weinberger, 2003). Maize and mungbean are predominantly grown in low-productive rainfed upland cropping systems and/or in rice-based lowland cropping systems with supplementary irrigation (Malaviarachchi et al., 2015). Even though farmers prefer to cultivate rice in lowlands, they are advised by the

officials from the government Departments of Agriculture and Irrigation to diversify the lands with other field crops such as maize and mungbean. This is to improve the aggregate productivity of lowland cropping systems where the irrigation water is not sufficient to cultivate all the lowlands with rice during the Yala season. Apart from inclusion as an intercrop with maize, cultivation of mungbean has recently been recommended as a third season crop by the Department of Agriculture, Sri Lanka. The recommendation is for mungbean as a sandwich crop after the Yala season rice crop and before the Maha season rice crop in selected irrigated paddy tracts so as to more effectively use residual soil moisture. Mungbean requires only 70–80 days for life cycle completion. However, at present, cultivation decisions such as the type of crops to be grown other than rice and their extents are being determined without a sound scientific basis (De Silva et al., 2015). In other words, farmers cultivate maize and mungbean as sole crops or mungbean as an intercrop with maize without a proper scientific base to understand the best options and their benefits and risks. Moreover, when crops are grown in combination or in rotation, the amount of irrigation water required for each crop per day and/or during the cultivation season, as well as the most efficient irrigation water application intervals, are not known. The WP differs among crops, varieties of a crop, irrigation management, soil and weather conditions, and cultivation method such as intercrop or sole crop systems. The biological basis for intercropping involves complementarity of resource-use by the two crops. Increasing productivity of intercropping of soybean [Glycine max (L.) Merr.] with maize, over the respective sole crops, has been attributed to better utilization of solar radiation (Keating and Carberry, 1993), nutrients (Willy, 1990), and water (Tsubo et al., 2005). Productivity of rice-chilli (Capsicum annuum L.) crop rotations have been studied in lowlands under resource-poor village tank irrigation systems of Sri Lanka in terms of land, water, capital and material costs (Marambe et al., 1999). However, the WP of the important cereal-legume intercropping systems compared to their respective sole crops under lowland rice-based cropping systems in South-Asia is not well understood. Moreover, the advantages of using mungbean as a sandwich crop between two major rice growing seasons utilising residual soil moisture still requires further research to determine productivity and risk. Therefore, this study addresses the important agronomic decisions that farmers must make before the beginning of a cropping season in selection of crop combinations and their extents in rice-based cropping systems so as to maximise the area to be cultivated and the productivity. The Agriculture Production System Simulator (APSIM) has been used for the evaluation of impacts of management practices on yield and soil resources, and fertiliser management of rice, maize and mungbean in different parts of the world (Robertson et al., 2000; Micheni et al., 2008; Gaydon et al., 2012a,b, 2017; Suriyagoda and Peiris, 2014). However, APSIM has not yet been parameterised or validated for maize and mungbean in Sri Lanka as a step towards evaluating improved management options. The pathways for achieving improved efficiency will differ among diverse cropping systems (Carberry et al., 2012). In order to arrive at sound management decisions on crop choices, the ability of APSIM to simulate growth, yield and water-use of crops (maize and mungbean) under moisture-limited field conditions requires evaluation. Amarasingha et al. (2015b) validated the APSIM-Oryza module for different rice varieties belonging to 3 and 3½ months duration crop classes grown in Sri Lanka. Therefore, the objectives of this study were to; (i) parameterise, calibrate and validate the APSIMmaize and APSIM-mungbean modules for widely grown varieties in Sri Lanka (for maize inbred local variety ‘Ruwan’ and imported hybrid variety ‘Pacific’, and local mungbean variety ‘MI6 ), (ii) simulate the water requirement for rice (3 months and 3½ months duration crops), maize, and mungbean under rice-based cropping

R.P.R.K. Amarasingha et al. / Agricultural Water Management 189 (2017) 111–122

Maha-Illuppallama

Rainfall (mm)

400

Weerawila

3rd season crop

Yala season

Maha season

400

300

300

200

200

100

100

0

0

400

400

Aralaganwila

300

300

200

200

100

100

0

0

Vanathawilluwa

400

113

Bathalagoda

Bombuwala 400

300

300

200

200

100

100

0

0

Fig. 1. Long term (1976–2011) monthly average rainfall for Maha-Illuppallama (Dry Zone), Aralaganwila (Dry Zone), Vanathawilluwa (Dry Zone), Weerawila (Dry Zone), Bathalagoda (Intermediate Zone), and Bombuwela (Wet Zone) of Sri Lanka, during the two cultivation seasons (i.e. Yala and Maha), and the additional third season for the sandwich mungbean crop.

Table 1 Description of the study sites. Location Aralaganwila Bathalagoda Bombuwela Maha-Illuppallama Maradankalla Vanathawilluwa Weerawila

Latitude ◦



07 76 07◦ 31 06◦ 34 08◦ 05 08◦ 14 08◦ 05 06◦ 02

10” N 26 N 23 N 57 N 46 N 10” N 42 N

Longitude ◦



87 17 80◦ 25 80◦ 00 80◦ 26 80◦ 42 79◦ 56 81◦ 23



47 E 57 E 44 E 34 E 52 E 20 E 39 E

Elevation (m AMSL) 166 115 32 111 126 32 16

system in the Dry Zone of Sri Lanka, (iii) evaluate the best combination of crops (cropping pattern) to maximise water productivity, and (iv) assess the risk of cultivating mungbean as the third season/sandwich crop in different regions in Sri Lanka. 2. Materials and methods 2.1. Description of the study area Most of the maize and mungbean production occurs in the Dry and Intermediate Zones of Sri Lanka while rice is grown in Dry, Intermediate and Wet Zones (Fig. S1, Table 1). The Dry Zone receives a mean annual rainfall less than 1750 mm with a relatively dry season from March to August (i.e. Yala season) (Amarasingha et al., 2015b) (Figs. 1, S1). On the other hand, the Wet Zone receives a mean annual rainfall greater than 2500 mm and is distributed

throughout the year without a distinct dry season while the Intermediate Zone has in between characteristics with respect to the amount and distribution of annual rainfall. 2.2. APSIM model parameterisation 2.2.1. APSIM model The APSIM-maize and APSIM-mungbean modules in APSIM version 7.5 were parameterised, calibrated and validated before being used to test different scenarios. 2.2.2. Input data 2.2.2.1. Crop data source for model parameterisation/calibration. Parameterisation was performed for APSIM-maize and APSIMmungbean modules for two widely-grown maize varieties (i.e. ‘Ruwan’ and ‘Pacific’) and one mungbean variety (i.e. ‘MI6 ) recommended by the Department of Agriculture, Sri Lanka. Data required for model parameterisation were obtained from NCVT (2014) at the Field Crops Research and Development Institute at MahaIlluppallama, Sri Lanka (Fig. S1), and are summarised in Table S8. 2.2.2.2. Soil data. Soil characteristics of Maha-Illuppallama site were obtained from Mapa et al. (2010) and are summarised in Tables S1. 2.2.2.3. Weather data. Daily weather data (maximum and minimum temperatures, rainfall, and sunshine hours) from January

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Table 2 Phenological parameters and cumulative thermal time to complete different developmental events of the two maize varieties. Parameter Descriptions

Cumulative thermal time

◦

Emergence to end of juvenile ( C days) Flower to maturity (◦ C days) Flag to flower (◦ C days) Flower to grain fill (◦ C days) Maturity to ripe (◦ C days) Grain growth rate (mg grain−1 day−1 )

‘Ruwan’

‘Pacific’

120 950 20 200 2 9

130 1050 20 120 2 15

Table 3 Phenological parameters and cumulative thermal time to complete different developmental events of the mungbean variety ‘MI6 . Parameter Descriptions

Cumulative thermal time

Emergence to end of juvenile phase (◦ C days) End of juvenile phase to floral initiation phase (◦ C days) Flowering to start grain filling (◦ C days) Maturity to harvest ripe (◦ C days)

350 1000 367 5

1976 to May 2014 for Maha-Illuppallama, were obtained from the Natural Resource Management Centre of the Department of Agriculture, Sri Lanka. Incoming radiation (MJ m−2 d−1 ) was calculated using sunshine hours and location-specific information i.e., latitude and longitude (Fig. S1), and angstrom coefficients (a = 0.29 and b = 0.39) (Samuel, 1991). 2.2.2.4. Crop management. Crop management practices considered were as recommended by the Department of Agriculture (DOA, 2014). The maize crop had a spacing of 60 cm × 30 cm with a sowing density of 5.5 plants m−2 , and spacing of the mungbean crop was 30 cm × 10 cm with a sowing density of 33 plants m−2 . Planting dates considered in the model simulations are given in Table S8. In the absence of rainfall to maintain soil moisture above the plant available water content (PAWC), irrigation was supplied at 7-day intervals until soil reached field capacity as this is the general practice. Thus, the crop would experience minimal moisture stress. Pest and disease incidence were assumed to be negligible due to imposition of control measures as recommended by the Department of Agriculture (DOA, 2014). In the intercrop, the density of maize was maintained similar to that of the sole maize crop (i.e. 5.5 plants m−2 ) while that of mungbean was reduced to 50% of the sole mungbean crop (i.e. 16.5 plants m−2 ). These densities were used in the simulations as farmers claim that the recommended density of maize is low. Both crops were simulated to be sown on the same day. All simulations were performed under recommended management practices. The third season (i.e. between Yala and Maha) mungbean crop was established with the density specified above for sole cropping conditions, one week after the harvest of the Yala season’s rice crop. Inorganic fertilisers and irrigation water were not applied. 2.2.3. Crop phenology Phenology and growth parameters of maize and mungbean were derived from the literature (ASDA, 2012 and references therein) (Tables 2 and 3). Thermal time accumulations were estimated by the algorithm described by Jones and Kiniry (1986) using the observed phenology and weather data. Base and optimal temperatures of maize were 8 and 34 ◦ C, respectively (Wilkens and Singh, 2001; Ranaweera et al., 2009), while those of mungbean were 10 and 30 ◦ C, respectively (Robertson et al., 2000; Kumudini et al., 2014).

2.3. Model validation 2.3.1. Soil, weather, crop and management data The APSIM-maize and APSIM-mungbean modules were validated using data collected at Aralaganwila, Maha-Illuppallama, Maradankalla and Weerawila. For this purpose, both primary (i.e. data collected during this study period and are not published elsewhere), and secondary (i.e. data collected from published sources) data were used. Soil characteristics of the study sites were obtained from Mapa et al. (2010) and are summarised in Tables S1–S7. Daily weather data from January 1976 to May 2014 for each site were obtained from the Natural Resource Management Centre of the Department of Agriculture, Sri Lanka. Only for Maradankalla, weather data from Maha-Illuppallama were used. Secondary data on grain yield collected from NCVT (2014) at Maha-Illuppallama were used for model validation purpose (Table S8). In addition, the primary grain yield data collected from (i) farmer field conditions at Maradankalla, under both supplementary irrigated and rainfed conditions, (ii) intercropping of maize and mungbean at MahaIlluppallama and Aralaganwila, and (iii) the third season mungbean crop from Weerawila were also used for validation purpose. All the crop management practices were similar to those detailed in Section 2.2. However, according to published studies (ASDA, 2012 and references therein), the density of maize and mungbean under intercropping systems were 2.75 plants m−2 and 16.5 plants m−2 , respectively, during the validation process. The model was validated for the supplementary irrigation water requirement, using primary data collected from Maradankalla and Maha-Illuppallama for maize and mungbean, and data collected from Bathalagoda, Maradankalla and Vanathawilluwa for rice. Model simulations were performed by applying irrigation water until the soil was simulated to reach field capacity for maize and mungbean, and to a submergence depth of 5 cm for rice at 7-day interval, only when rainfall did not occur. 2.3.2. Statistical evaluation We used linear regression to compare paired data-points for field observed and simulated grain yields and crop water use by and determining the slope (␣), intercept (␤), and coefficient of determination (R2 ). The model performance (probability of significant difference between simulated and measured values at 95% confidence) was evaluated using the Student’s t test of means assuming unequal variance P(t). The root of the mean squared error was calculated (RMSE; mean differences between the values predicted by the model and the values observed) using;

   n  (Si − Oi )2

RMSE =

i=1

n

Where Si and Oi are simulated and observed values, respectively, and n is the number of pairs. A model reproduces experimental data best when the slope (␣) is 1, the intercept (ˇ) is 0, R2 is 1, P(t) is larger than 0.05 (indicates that observed and simulated data are same at the 95% confidence level), and the absolute RMSE is similar to the standard deviation of experimental measurements. 2.4. Scenario analysis 2.4.1. Water requirement in rice-based cropping systems The APSIM-maize and APSIM-mungbean modules parameterised and validated in this study, and APSIM-oryza module that was validated previously (Amarasingha et al., 2015b) were used for scenario analysis. Model simulations were performed to estimate the yield and water requirement under a sole cropping system for

R.P.R.K. Amarasingha et al. / Agricultural Water Management 189 (2017) 111–122 Table 4 Percentage land extent covered by different crop combinations as used by farmers. Combination

Rice

Maize

Mungbean

Intercropping

1 (R100) 2 (R75:M25) 3 (R75:Mb25) 4 (R75:I25) 5 (R50:Mb50) 6 (R50:M50) 7 (R50:I50) 8 (R25:M50:Mb25) 9 (R25:M25:Mb50) 10 (R25:I75) 11(M50:Mb50) 12 (M50:Mb25:I25) 13 (M50:I50)

100 75 75 75 50 50 50 25 25 25 0 0 0

0 25 0 0 0 50 0 50 25 0 50 50 50

0 0 25 0 50 0 0 25 50 0 50 25 0

0 0 0 25 0 0 50 0 0 75 0 25 50

Note: R- rice, M- maize, Mb- mungbean, I- intercropping of maize and mungbean and the number denotes the percentage of land-use by each crop or crop combination.

the short (‘Bg300 – 3 months to maturity) and medium (‘Bg359 – 3 1/2 months to maturity) duration rice varieties, maize (variety ‘Ruwan’) and mungbean (variety ‘MI6 ), and the maize-mungbean intercropping system under two scenarios when grown under water limited Yala season with supplementary irrigation (Fig. 1). Two scenarios were compared; Scenario 1: supplementary irrigation was applied when soil moisture in the top 60 cm reached 25% of plant available water content (PAWC), and Scenario 2: supplementary irrigation was applied at 7-day intervals until soil reaches field capacity each time (i.e. farmers practice). The APSIM-Oryza model was configured with the soil being puddled, fertilised and levelled. Inorganic fertilisers were simulated as recommended by the Department of Agriculture (DOA, 2014). The pre-germinated seeds were considered as randomly broadcast to a density of 90 plants m−2 , while pest and disease incidences were considered as controlled using the recommended practices by the Department of Agriculture (DOA, 2014). Simulations were performed for Maha-Illuppallama for 37 years. Grain yield and water requirement for sole crops of rice, maize and mungbean, and maizemungbean intercropping systems were simulated. Land Equivalent Ratio (LER) was calculated to determine the productivity of intercropping system in comparison to respective sole crops. The irrigation water requirement per day was calculated using; Irrigation water requirement (mm day−1 ) =

Total irrigation water requirement (mm) Crop duration (days)

2.4.2. The best combination of crops; giving priority to rice while maximising WP Simulations were performed for the Yala season as lands are being diversified with different field crops other than rice due to the limitation of water. Thirteen crop combinations were tested based on the farmers’ practice in the region (Table 4), considering that the current selection of crops or crop combinations by the farmers is not based on improving the WP, but based on maximising profits, availability of labour and/or time requirement to establish and manage crops. The WP was calculated for different crop combinations using the simulated yield and water required during the cropping season (through rainfall and supplementary irrigation) using the formula;

115

The WP of maize-mungbean intercrop was calculated using the separate yields of maize and mungbean and the amount of water used for the duration of maize crop in the said land area. Net return from different crops or crop combinations was calculated as the difference between gross income and total cost. Total cost and income were calculated using the monetary data available in the literature (AgStat, 2013) i.e. selling prices of rice, maize and mungbean grains were 45, 35 and 158 Sri Lankan Rupees (SLR) per kg, respectively. Financial return from irrigation water was calculated as; Return from irrigation water (SLR ha−1 mm−1 ) =

Net return (SLR ha−1 ) Amount of irrigation water applied (mm)

2.4.3. Performance of mungbean as a third season crop (end of Yala season) in different climatic zones of Sri Lanka The time gap after the harvest of the Yala season rice crop and before the onset of the Maha season rain is identified as the third season (Fig. 1). The expectation of growing a short duration mungbean crop was to utilise the residual soil moisture available at the end of the Yala season rice crop. Simulations were performed for 37 years to evaluate mungbean yield as a third season crop in different locations (i.e. Maha-Illuppallama (Dry Zone), Aralaganwila (Dry Zone), Vanathawilluwa (Dry Zone), Weerawila (Dry Zone), Bathalagoda (Intermediate Zone), and Bombuwela (Wet Zone)). Mungbean was sown one week after the harvest of the Yala season rice crop at a density of 33 plants m−2 . Crop management practices were similar to those described in the model parameteristation and validation sections. The risk of the third season’s mungbean crop was calculated as the ratio of mungbean yield obtained in the third season without irrigation and yield for the Yala season with irrigation (i.e. standard practice) and expressed as a percentage of yield loss. The simulated evapotranspiration (ET) for the third season’s mungbean crop was compared with the potential evaporation after modification for weeds (i.e. eos according to APSIM notation) and bare soil evaporation (i.e. es according to APSIM notation) for the same period. Widely observed weeds were; Echinochloa colonum (L.) Link, Echinochloa crus-galli (L.) Beauv, Panicum repens L., Commelina diffusa Burm.f., and Eclipta prostrata (L.). The yield of the third season mungbean crop either after a short (‘Bg300 ) or medium (‘Bg359 ) duration rice variety grown in the Yala season was also calculated. Net return from different crops or crop combinations grown at Maha-Illuppallama, and the simulated third season’s mungbean yield from different locations were tested using one-way ANOVA. Two-way ANOVA was used to compare; (i) simulated irrigation water requirement for different crops or crop combinations at Maha-Illuppallama under two irrigation water application scenarios, and (ii) ET from the third season’s mungbean crop or ET from the fallow field with weeds (i.e. eos) or without weeds (i.e. es) at different sites. Simulated values obtained from 37 years were considered as replicates for each site. Mean separation was done using DNMRT procedure and all the interpretations were made at ˛ = 0.05. 3. Results 3.1. APSIM model parameterisation

Simulated WP (kg ha−1 mm−1 ) =

(kg ha−1 )

Yield Water use for crop duration (SI + RF) (mm)

Phenological parameters used when parameterising maize varieties; ‘Ruwan’ and ‘Pacific’, and mungbean variety ‘MI6 are given in Tables 2 and 3.

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Simulated yield (kg ha-1)

4500 Maize - Ruwan 4000 3500 3000

Simulated yield (kg ha-1)

2500 2500

8000

3000 3500 4000 Observed yield (kg ha-1)

4500

Maize - Pacific

3.3. Validation of supplementary irrigation water requirement

7000 6000

4000

3000

The parameterised APSIM could simulate and explain a high fraction of the supplementary irrigation water requirement with an R2 of 0.9 (Fig. 3). The RMSE was 66 mm season−1 ha−1 . This compares favourably with the observed experimental variability (standard deviation amongst replicates of 203 mm season−1 ha−1 ), and hence the model predictions were well within the limits of experimental uncertainty. The results of the Student’s t-test confirmed the similarity between the field-observed and modelsimulated supplementary irrigation water requirement (P > 0.05). Crop yield data obtained from the experiments used to validate supplementary irrigation water requirement further illustrated the capacity of the parameterised APSIM modules to simulate growth of maize, mungbean and rice under the relevant crop management (Fig. 3).

R2 = 0.98 CV= 9.7 RMSE = 542 P value = 0.5

5000

3000 3000

Simulated yield (kg ha-1)

R2 = 0.97 CV= 5.5 RMSE = 182 P value = 0.7

4000 5000 6000 7000 Observed yield (kg ha-1)

8000

Mungbean - MI6

2500

3.4. Simulated crop water requirements

2000 R2 =

1500 1000

0.98 CV= 3.8 RMSE = 75 P value = 0.88

500 500

ered acceptable to simulate the phenology and growth of widely cultivated inbred (i.e. ‘Ruwan’) and hybrid (i.e. ‘Pacific’) maize varieties, but in resource-limited areas in the Dry Zone of Sri Lanka. The parameterised APSIM-mungbean model could simulate and explain a high fraction of the grain yields and observed variability of ‘MI6 with R2 of 0.98 (Fig. 2) with a RMSE of 75 kg ha−1 . This compares very favourably with the observed experimental variability (standard deviation amongst replicates of 667 kg ha−1 ), and hence the model predictions were well within the limits of experimental uncertainty. Comparisons between the field-observed and modelsimulated grain yields using Student’s t-test for one of the varieties confirmed similarity between yield values (P > 0.05). Therefore, the parameterised APSIM-mungbean model can also be considered acceptable to simulate the phenology and growth of ‘MI6 under the relevant crop management.

1000 1500 2000 2500 Observed yield (kg ha-1)

3000

Fig. 2. Observed and simulated sole crop yields of local inbred maize variety ‘Ruwan’, hybrid maize variety ‘Pacific’ and mungbean variety ‘MI6 in Yala (unfilled symbols) and Maha (filled symbols) seasons at Maha-Illuppallama (circles) and Maradankalla (squares), and when grown in intercropping system (crosses) at Maha-Illuppallama (‘Ruwan’) and Aralaganwila (‘Pacific’) and as a third season crop (triangle) at Weerawila, Line indicates the 1:1 relationship.

3.2. Validation of APSIM-mungbean and APSIM-maize models The relationships between field-observed and model-simulated grain yield values were strong at the validation stage for both maize varieties (i.e. R2 values of 0.98 for both) as shown in Fig. 2. This high correlation shows that the parameterised APSIM-maize could explain a large fraction of the total field-observed variability in grain yields. Comparisons between field-observed and modelsimulated grain yield values using Students t-test for two varieties confirmed acceptable model performance (P > 0.05), indicating no statistical difference between simulated and measured values at a 95% confidence level. Similarly, RMSE for maize varieties ‘Ruwan’ and ‘Pacific’ were 182 and 542 kg ha−1 , respectively, which was assumed to be within the bounds of experimental uncertainty. Therefore, the parameterised APSIM-maize model can be consid-

When the two irrigation scenarios were compared, irrigation water requirement during the cultivating season was higher when fields were irrigated based on soil moisture depletion than when irrigated in a regular 7-day interval for rice and the intercropping system (Fig. 4). However, the irrigation water requirement for the intercrop was similar to that of the sole maize crop. Irrespective of the irrigation scenarios, the water requirement was the highest for the rice crop (1520 mm and 1180 mm for ‘Bg359 and ‘Bg300 , respectively) and the lowest for mungbean (345 mm). Maize and mungbean required 54% and 78% less irrigation water, respectively, than that was required by the medium duration rice variety (‘Bg359 ) for scenario 2. The maize-mungbean intercrop required 3% more water (672 mm) than that was required for the sole maize crop (637 mm) (P > 0.05), and 48% more than the sole mungbean crop under regular irrigation practice (P < 0.05). The medium duration rice variety (‘Bg359 ) required more irrigation water than the short duration variety (‘Bg300 ) i.e. 23% and 27% under the scenarios 1 and 2, respectively as expected. Irrespective of the irrigation scenario, water requirement per day was higher for rice, as expected, than that for maize and mungbean grown either as sole crops or as an intercrop (Fig. 4). Maize and mungbean sole crops or their intercrop required 54%, 57% and 50% lower amounts of water per day, respectively, than that required by the short and medium duration rice varieties when the irrigation interval was 7-days. The daily water requirement for sole maize and mungbean crops and their intercrop was similar. The sole crop yield of rice and the intercropped yields (maize and mungbean) were similar between the two irrigation scenarios, while the sole crops of maize and mungbean resulted higher yields for scenario 1 compared to that for scenario 2 (Fig. 5). The medium duration rice variety ‘Bg359 reached a higher grain yield compared

Maradankalla (Rice Medium) Vanathawilluwa (Rice Medium) Bathalagoda (Rice Medium) Bathalagoda (Rice Short) Maradankalla (Sole crop Maize) Maradankalla (Sole crop Mungbean) Maha-Illuppallama (Intercrop M+MB)

1000

750 R2 = 0.90 CV =10.92 RMSE = 65.94 P value = 0.8

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Maradankalla (Rice Medium) Vanathawilluwa (Rice Medium) Bathalagoda (Rice Medium) Bathalagoda (Rice Short) Maradankalla (Sole crop Maize) Maradankalla (Sole crop Mungbean) Maha-Illuppallama (Intercrop M) Maha-Illuppallama (Intercrop MB)

8000

Simulated yield (kg ha-1)

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6000

4000 R2 = 0.97 CV =6.2 RMSE = 276.22 P value = 0.8

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ha-1)

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Simulated irrigation water requirement (mm day-1)

Simulated irrigation water requirement for crop duration (mm)

Fig. 3. Field-observed and model-simulated supplementary irrigation water requirement and crop yields of maize (M), mungbean (MB) and rice (for short and medium duration rice varieties) in Yala (unfilled symbols) and Maha (filled symbols) seasons at different locations. Line indicates the 1:1 relationship.

Scenario 2

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8

4

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Scenario 2

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Fig. 4. Simulated irrigation water requirement for different crops, and cultivation systems (i.e. sole crops of rice, maize and mungbean or intercrop of maize and mungbean) during Yala season at Maha-Illuppallama under the two scenarios; Scenario 1. Irrigate when plant available water content (PAWC) in soil reached to 25%, and Scenario 2. Irrigate in regular 7-day intervals reflecting farmer practice. mean ± s.e. n = 37.

Scenario 1

to that of the short duration rice variety ‘Bg300 irrespective of the irrigation scenario. Even though the intercropped maize yield was similar to its sole crop yield, the mungbean yield under intercropping was reduced by 21% compared to its sole crop yield. The LER value for maize-mungbean intercropping system was 1.76 ± 0.23.

Scenario 2

Yield (kg ha-1)

6000

4000

3.5. The best combination of crops to maximise WP: compromise between rice, and maize and/or mungbean

2000

Irrigation water requirement was highest when land was used only for rice cultivation (Fig. 6). Cultivation of 50% of the land with maize and the rest with mungbean as sole crops required the least supplementary irrigation, which was only 40% of the water requirement for rice cultivation covering the whole land area. Mungbean required the least amount of water and had the highest WP when grown as a sole crop. The total WP of the system increased when mungbean and maize crops were incorporated into the system while reducing the% land extent covered by rice (Fig. 6). Therefore, the presence of maize and mungbean in the system covering over 75% of the land, either as sole crops or an intercrop, resulted in the highest WP (i.e. over 65% of the WP of rice). Rice and mungbean grown as sole crops, each occupying 50% of the land area, resulted in the highest net return, while 50% rice and 50% maize

0 Bg300 Bg300

Bg359 Bg359 Rice

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MI6 MI6

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Ruwa n Ruwan

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Fig. 5. Model-simulated yield of sole crops of rice varieties ‘Bg300 and ‘Bg359 , maize varieties ‘Ruwan’, and mungbean variety ‘MI6 , and maize-mungbean intercrop when irrigated as the plant available water content (PAWC) in soil reached to 25% (Scenario 1) or at a regular interval of 7-days (Scenario 2) at Maha-Illuppallama, mean ± s.e. n = 37.

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Fig. 6. Simulated irrigation water requirement, simulated water productivity (WP), and net return from different crop combinations and net return per unit of irrigation water applied when land is being cultivated with different sole crop combinations or in an intercropping system during the Yala season at Maha-Illuppallama, Note: R – rice, M – maize, Mb – mungbean, I – intercropping of maize and mungbean and the number denotes the percentage of land-use by each crop or crop combination from 1976 to 2013, mean ± s.e. n = 37.

resulted in the lowest net return. The net return per irrigation water applied was the highest when sole maize and mungbean crops were grown at 50% of the land extent by each crop (115 Sri Lankan Rupees ha−1 mm−1 ). In contrast, rice grown over the whole land area (100%), sole rice and maize crops grown in 75:25 area ratio, or sole rice and maize crops at 50:50 area ratio recorded the lowest net return per unit of irrigation water used (Fig. 6). Importantly, all these crop combinations were associated with similar level of risk in terms of their variability (Fig. 6).

3.6. Evaluation of mungbean as a third season crop Simulated grain yields of the third season (mungbean) crop at Maha-Illuppallama (Dry Zone), Aralaganwila (Dry Zone), Bathalagoda (Intermediate Zone) and Bombuwela (Wet Zone) were similar, but lower at Vanathawilluwa (Dry Zone) and Weerawila (Dry Zone) (Fig. 7). However, the variability of grain yield at MahaIlluppallama and Aralaganwila was higher than that observed at other sites. The risk of third season mungbean cultivation, when expressed as% yield loss in comparison to the yield obtained in the Yala season at that site, was only 4% and 3%, at Bathalagoda and Bombuwela sites, respectively. However, Vanathawilluwa, Maha-

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1600 Yield (kg ha-1)

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119

1200 800

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9 Bg35 Bg359

Mungbean B g35 9 Bg3 59

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Fig. 9. Simulated grain yield of short (‘Bg300 ) and medium (‘Bg359 ) duration rice varieties when cultivated during the Yala season at Maha-Illuppallama, and the third season mungbean crop yield after the cultivation of either ‘Bg300 or ‘Bg359 for the period from 1976 to 2013. mean ± s.e. (n = 37). Fig. 7. Simulated grain yield of mungbean when cultivated as a third season crop at the end of the Yala season in different locations representing diverse agro- climatic zones for the period from 1976 to 2013, mean ± s.e. n = 37.

4. Discussion 4.1. Performance of APSIM-maize and APSIM-mungbean modules under water-limited growing conditions

Evapotranspiration (mm day−1)

ET (Weeds)

Soil evaporation

ET (Mungbean)

8

6

4

2

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Fig. 8. Average daily evapotranspiration (ET) during the period of the third season mungbean crop was grown and fallow field (with and without weeds) for the same duration at different locations representing diverse agro-climatic zones for the period from 1976 to 2013. mean ± s.e. (n = 37).

After adjusting a few key phenological parameters (Tables 2 and 3), the APSIM-Maize and APSIM-Mungbean modules were able to accurately simulate the development and growth of both crops. Here, model parameterisation and calibration were performed using the data collected at Maha-Illuppallama representing different cultivation seasons and management while the model validation was performed using the data collected from different locations. The strong fit between the field-observed and model-simulated yields and supplementary irrigation water requirement from diverse environments and management conditions indicate the ability of the parameterised APSIM to capture the variability of grain yield and irrigation water requirement observed in the field. The levels of precision achieved when simulating the maize grain yield in Australia, China and Ghana are comparable with the values in the present study (Peake et al., 2008; Fosu-Mensah et al., 2012; Liu et al., 2012; Wang et al., 2014). The model-simulated mungbean grain yield in the present study is higher than that was obtained in Australia (Robertson et al., 2002) reflecting the better growing environment in Sri Lanka and the selection of correct agronomic conditions in our study. Thus, the validated models can be used to predict with confidence, potential outcomes under different environments and management options. 4.2. Variation of crop water requirements and WPs among crops

Illuppallama, and Aralaganwila had high yield losses (i.e. 25, 20 and 19%, respectively). All these three sites are located in the Dry Zone of Sri Lanka. The average daily ET during the period when the third season mungbean crop was grown, was similar to that at fallow site when weeds were present (i.e. eos) (Fig. 8). However, the simulated ET from both mungbean and fallow site with weeds was higher than the bare soil evaporation (i.e. es) for the same period. The highest ET values were found at Vanathawilluwa and Weerawila sites and the lowest at Bathalagoda and Bombuwela sites. The third season mungbean yield obtained after a short duration rice crop (i.e. three months age class) was 30% higher than that obtained after a medium duration (three and half month age class) rice variety grown in the Yala season (Fig. 9).

Selection of crops based on CP and WP in rice-based cropping systems has merits. The total water requirement during the cropping season was greater for rice than for the intercropping of maize and mungbean or for maize alone, whilst mungbean had the lowest requirement. As the daily water requirement for maize and mungbean grown either as sole crops or as an intercrop was similar, the selection of the crop (i.e. whether to cultivate maize or mungbean) and the system of cultivation (i.e. sole crop or intercrop) should be based on differences in crop duration, available irrigation water in the reservoir and expected net financial return. Given the fact that rice requires 2–3 times more water than maize, and 4–5 times more water than mungbean, there is considerable flexibility to adopt water efficient irrigation and crop management

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strategies depending on seasonal conditions and water availability. This could be of great importance to ensure the high land productivity of water limited lowland cropping systems (Gheewala et al., 2014). Irrigation water requirement for sole crops of maize and mungbean was similar between when irrigating the crop when PAWC in the soil depletes to 25% (scenario 1) and at regular 7-day irrigation interval (scenario 2). However, rice and maize and mungbean intercrops required more irrigation water for scenario 1 than for scenario 2. When rice was irrigated under scenario 1, the grain yield also increased by 20% and 9% for the short and medium duration rice varieties, respectively, in comparison with scenario 2. This response resulted from the higher water stress experienced under scenario 2 by the rice crop during the period from the photoperiod sensitive phase to grain filling phase (data not shown). Therefore, the current irrigation interval used in rice cultivation in the Dry Zone of Sri Lanka is not the most efficient with respect to WP, but may be considered as a simple method to implement by farmers or water managers. These results emphasise a need for capacity development amongst farmers for implementing alternative irrigation management techniques such as scheduling based on the actual soil moisture depletion and readily available moisture during critical periods of plant development. 4.3. The best combination of cropping patterns for minimum water usage and high CP and WP The best crop or crop combination for water-limited rice based cropping systems should have a low irrigation water requirement leading to high net return per unit of irrigation water used, i.e. high WP. Based on these determinants, the most efficient crop combinations maximising net return and WP are; (i) 50% rice and 50% mungbean as sole crops, or (ii) 25% rice, 25% maize and 50% mungbean as sole crops, respectively. Importantly, these improved productivity, efficiency and financial returns are achieved at similar levels of risk as less efficient practices. As the intercropping system did not outperform the respective sole crops with respect to the irrigation water use and net return, cultivating above crop combinations as sole crops with proper land selection would make the management easier for farmers than incorporating intercropping into rice-based cropping systems. The results also indicated that under moisture deficit conditions, the lowland paddy tract could be totally cultivated to 50% maize and 50% mungbean as sole crops if WP would be the prime consideration. Cultivating rice over the total land extent was the most inefficient crop or crop combination with respect to both irrigation water requirement and net return, followed by 50% rice and 50% maize. Similarly, cultivating 75% of the land with rice and the remainder with either sole crops of maize or mungbean, or intercropping maize and mungbean was still inefficient, requiring high amounts of irrigation water and low net return for the irrigation water used. Inefficiency of the above crops or crop combinations was partly due to the lower farm-gate prices of rice and maize compared with that of mungbean, apart from the differences in crop durations, agronomy and crop physiology. Even though the rice crop required more supplementary irrigation and achieved low net return per unit of irrigation water, cultivating rice is still given priority (even under water-limited situations) due to the cultural importance and its contribution to household food security (DOA, 2014; De Silva et al., 2015). Therefore, decisions made on the types and ratios of crops to be grown in rice-based cropping systems must consider the minimum land extent, which needs to be dedicated to rice for achieving self-sufficiency under current productivity and management. The remainder of the land would then be allocated to other crops such as maize and mungbean, which use less water while providing higher

net income. The results of our modelling analysis opens up a new avenue for discussion and decision making with a sound scientific basis to secure the required lands for rice cultivation while allowing farmers to choose the best alternative crops for the remaining lands to achieve the maximum net return by achieving high WP and CP. 4.4. Risk assessment of the third season/sandwich mungbean cultivation The model-simulated mungbean yields for the Yala season varied in the range of 1250 kg ha−1 and 3802 kg ha−1 , at different locations in Sri Lanka. On the other hand, model-simulated mungbean yield for the third season varied within the range of 870 kg ha−1 to 1360 kg ha−1 , which was 30% to 64% lower than that achieved in the Yala season. The lower average and the higher variability of the third season’s mungbean yield could be due to differences in agro-climatic conditions across locations i.e. Bathalagoda and Bombuwela, being located in the Intermediate Zone and Wet Zone in the country, respectively. The Intermediate Zone and Wet Zone areas experience higher rainfall and lower ET during the Yala season while Vanathawilluwa and Weerawila (both in Dry Zone) had the least amount of rainfall and higher ET. Thus, the third season’s mungbean crop recorded comparatively low yield in Dry Zone areas during the Yala season due to high moisture stress in comparison to Intermediate Zone and Wet Zone areas. Similarly, the variability observed in mungbean yield at Maha-Illuppallama and Aralaganwila sites could be due to the high variability of rainfall and ET during the Yala season experienced in these two sites, compared to the other sites used in this study. Therefore, locations in the Wet Zone and Intermediate Zone can be characterised by relatively low risk with high yield potentials for the third season’s mungbean crop, while locations in the Dry Zone with high ET and low and highly variable rainfall values are expected to have consistently low yield potentials and be more risky. The benefit of having a third season mungbean crop in ricebased cropping systems can be enormous (Jayawardena and Jayasinghe, 1992), however this option does not suit all situations (Balwinder-Singh et al., 2015; Malaviarachchi et al., 2015). In Sri Lanka, farmers do not apply inorganic fertilisers or irrigation water to the third season mungbean crop, relying on the minimum crop management practices such as control of pest and diseases as required. As the amount of water loss from the mungbean crop was similar to the fallow field, the soil moisture dynamics of the cropping system are effectively unaltered by incorporation of the mungbean crop into the rotation. Moreover, the strategy may have additional agronomic benefits such as those resulting from incorporation of nitrogen into the soil through symbiosis (Sangakkara, 1994; Masikati et al., 2014). Due to this strong potential benefit and low cost, our modelling indicates the importance of testing the applicability of incorporating a third season, short-duration sandwich crop such as mungbean in rice-based cropping systems – aiming to improve the overall productivity of the system through utilising the residual soil moisture more efficiently. 5. Concluding remarks Parameterised, calibrated and validated APSIM maize and mungbean modules performed well. Using these modules, we identified cropping options that reduce water use, and increase net returns in water limited environments while meeting cultural and food security needs. Specifically this could involve increased proportional allocation of land to maize and mungbean crops as the WP can be improved over 65% when maize and/or mungbean is covered by at least 75% of the extend than cultivating rice alone.

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Model-simulated water requirement during the cropping season was in the order of rice > maize and mungbean intercrop = maize sole crop > mungbean sole crop. Additional benefits achieved on water use by incorporating more complex intercropping options tested here were found to be low. The strong benefit-cost ratio of including a third ‘sandwich’ crop of mungbean warrants further testing, noting that the viability will differ based on rainfall zone, with the Wet Zone and Intermediate Zone being more prospective than the Dry Zone. Acknowledgements Authors acknowledge the funding received from the AusAIDCSIRO project “Improved climate forecasting to enhance food security in Indian Ocean Rim countries” (AusAID Agreement 59553) through the Agriculture Education Unit (AEU) of the Faculty of Agriculture, University of Peradeniya to conduct the study, and the Department of Agriculture, Sri Lanka for providing the access to collect secondary data on crop performances and management, and weather. Constructive comments given by the reviewers to an earlier version of the manuscript are also acknowledged. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agwat.2017.05. 002. References ASDA, 2012. Proceeding of Annual Symposium of the Department of Agriculture , Sri Lanka. AgStat, 2013. Socio Economics and Planning Centre. Department of Agriculture, Sri Lanka. Amarasingha, R.P.R.K., Galagedara, L.W., Marambe, B., Silva, G.L.L.P., Punyawardena, R., Nidumolu, U., Howden, M., Suriyagoda, L.D.B., 2014. Aligning sowing dates with onset of rains improve rice yields and water productivity: modelling Oryza sativa L. in Maha season in the dry zone of Sri Lanka. Trop. Agric. Res. 25, 237–246. Amarasingha, R.P.R.K., Galagedara, L.W., Marambe, B., Silva, G.L.L.P., Punyawardena, R., Nidumolu, U., Howden, M., Suriyagoda, L.D.B., 2015a. Modelling the impact of changes in rainfall distribution on the irrigation water requirement and yield of short and medium duration rice varieties using APSIM during maha season in the dry zone of Sri Lanka. Trop. Agric. Res. 26, 274–284. Amarasingha, R.P.R.K., Suriyagoda, L.D.B., Marambe, B., Gaydon, D.S., Galagedara, L.W., Punyawardena, R., Silva, G.L.L.P., Nidumolu, U., Howden, M., 2015b. Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka. Agric. Water Manage. 160, 132–143. Balwinder-Singh, Humphreys, E., Sudhir-Yadav, Gaydon, D.S., 2015. Options for increasing the productivity of the rice-wheat system of north-west India while reducing groundwater depletion. Part 1. Rice variety duration, sowing date and inclusion of mungbean. Field Crops Res. 173, 68–80. Bouman, B.A.M., Tuong, T.P., 2001. Field water management to save water and increase its productivity in irrigated lowland rice. Agric. Water Manage. 49, 11–30. Bouman, B.A.M., Feng, L.P., Tuong, T.P., Lu, G.A., Wang, H.Q., Feng, Y.H., 2007. Exploring options to grow rice using less water in northern China using a modeling approach—II. Quantifying yield, water balance components, and water productivity. Agric. Water Manage. 88, 23–33. Carberry, P.S., Liang, W., Twomlow, S., Holzworth, D.P., Dimes, J.P., McClelland, T., Huth, N.I., Chen, F., Hochman, Z., Keating, B.A., 2012. Scope for improve eco-efficiency varies among diverse cropping system. Proc. Natl. Sci. U. S. A. 110, 8381–8386. Cassman, K.G., Olk, D.C., Doberman, A., 1997. Scientific evidence of yield and productivity declines in irrigation rice systems in tropical Asia. IRC Newsl. 46, 7–27. Cho, Y.S., Hidaka, K., Mineta, T., 2003. Evaluation of white clover and rye grown in rotation with no-tilled rice. Field Crops Res. 83, 237–250. DOA, 2014. Department of Agriculture (available at https://www.doa.gov.lk/index. php/en/croptechnology Accessed on 2014 August 26). De Silva, A.G.D.D., De Silva, G.N.N., Wijesinghe, A.J.A., Waas, M.W.P.K., Haddewla, P.S., Perera, A., 2015. Agriculture Prediction and Decision Support System for Sri Lanka (Available at http://dspace.sliit.lk/hanle/bitstream/12345678/288/1/ research%20Paper%20APDSS.docx Accessed on 2016 February 2003). Dobermann, A., Witt, C., 2000. The potential impact of crop intensification on carbon and nitrogen cycling in intensive rice systems. In: Kirk, G.J.D., Olk, D.C.

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