Irrig Sci (2013) 31:271–283 DOI 10.1007/s00271-011-0296-y
ORIGINAL PAPER
AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts Yashvir S. Chauhan • Graeme C. Wright Dean Holzworth • Rao C. N. Rachaputi • Jose´ O. Payero
•
Received: 12 October 2009 / Accepted: 1 July 2011 / Published online: 17 September 2011 Ó Her Majesty the Queen in Right of Australia as represented by The Government of Queensland 2011
Abstract Peanut (Arachis hypogaea L.) is an economically important legume crop in irrigated production areas of northern Australia. Although the potential pod yield of the crop in these areas is about 8 t ha-1, most growers generally obtain around 5 t ha-1, partly due to poor irrigation management. Better information and tools that are easy to use, accurate, and cost-effective are therefore needed to help local peanut growers improve irrigation management. This paper introduces a new web-based decision support system called AQUAMAN that was developed to assist Australian peanut growers schedule irrigations. It simulates the timing and depth of future irrigations by combining procedures from the food and agriculture organization (FAO) guidelines for irrigation scheduling (FAO-56) with those of the agricultural
production systems simulator (APSIM) modeling framework. Here, we present a description of AQUAMAN and results of a series of activities (i.e., extension activities, case studies, and a survey) that were conducted to assess its level of acceptance among Australian peanut growers, obtain feedback for future improvements, and evaluate its performance. Application of the tool for scheduling irrigations of commercial peanut farms since its release in 2004–2005 has shown good acceptance by local peanuts growers and potential for significantly improving yield. Limited comparison with the farmer practice of matching the pan evaporation demand during rain-free periods in 2006–2007 and 2008–2009 suggested that AQUAMAN enabled irrigation water savings of up to 50% and the realization of enhanced water and irrigation use efficiencies.
Communicated by P. Waller. Y. S. Chauhan (&) Department of Employment, Economic Development and Innovation (DEEDI), PO Box 23, Kingaroy, QLD, Australia e-mail:
[email protected] G. C. Wright Peanut Company of Australia, PO Box 26, Kingaroy, QLD 4610, Australia D. Holzworth CSIRO, PO Box 102, Toowoomba, QLD 4350, Australia R. C. N. Rachaputi Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland (UQ), PO Box 23, Kingaroy, QLD 4610, Australia J. O. Payero Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland (UQ), PO Box 102, Toowoomba, QLD 4350, Australia
Introduction In Australia, peanuts (Arachis hypogaea L.) have traditionally been grown under dryland conditions. However, due to recurring droughts over the last decade and increased risk of aflatoxin contamination associated with local climate change impacts (Chauhan et al. 2010), the area sown to dryland peanuts has declined significantly. To adapt to this impact, the industry has been transforming itself by shifting its production base from the dryland areas in the South Burnett (26.6°S, 152°E) and North Burnett (25.6°S, 151.6°E) districts to irrigated production regions throughout Queensland and the Northern Territory (Fig. 1). The key areas growing irrigated peanuts are now located in the Atherton Tableland (17°S, 145°E) and Georgetown (18.3°S, 143.5 °E) in northern Queensland; Bundaberg (*25°S, 152.5°E) and Childers (25.2°S, 152.3°E) in
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southern coastal Queensland; Emerald (23.5°S, 148.2°E) and the Mackenzie Valley to the east of the Mackenzie river (23°S 148.8°E) in central Queensland; Chinchilla (26.7°S, 150.50°E), Inglewood (29.5°S, 150.8°E), Texas (28.8°S, 151.2°E), and St. George (28°S, 148.6°E) in southern inland Queensland; and in Katherine (14.5°S, 132.2°E) in the Northern Territory. These areas grow peanuts on more than 5,000 ha and produce nearly 75% of the total Australian peanut crop. There are about 120 peanut growers in the irrigated production regions, including 30 in the Bundaberg region compared to about 60 in the dryland Burnett region. Most peanuts fields are irrigated with center pivots or winches, but a small proportion are also flood-irrigated in areas where irrigation water is abundant. Australia’s annual demand for peanuts is about 50,000 tons (of pods), which has been increasing at the rate of 2–3% per year. The increasing demand has created an urgent need to increase production, not only to cater to the rising domestic demand, but to also recapture the lucrative export market, which was lost due to the decline in dryland production. The yield potential of peanuts at these irrigated regions has been estimated at about 8 t ha-1 (Wright et al. 2001), but actual irrigated yield averages about 5 t ha-1 in the Bundaberg region, which barely exceeds the local economic break-even point of around 4 t ha-1. The contributing factors to low pod yield include inadequate or uneven plant populations, suboptimal planting and harvest times, harvest losses, foliar diseases (Bell 1986), weeds (Wright
Fig. 1 A map of Australia showing transformation of peanut cultivation from dryland areas of the North and South Burnett districts to other irrigated areas in Queensland (QLD) and the Northern Territory (NT) shown by black round symbols
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et al. 2001), and most importantly, poor irrigation management, which is known to cause significant pod yield reduction (Wright et al. 1991). Most peanut growers in Australia tend to irrigate after they observe crop wilting during the day and the amount of irrigation they apply is determined empirically based on grower’s experience. Other growers in the area apply irrigation to roughly meet the pan evaporation demand during rain-free periods, which may not be entirely adequate in all cases. These methods are not precise and could result in reduced pod yield (Gorbet and Rhoads 1975; Jones 2004; Crosthwaite 1994) and increase the risk of aflatoxin contamination if stress occurs during the last month of pod filling (Craufurd et al. 2006; Chauhan et al. 2010). Aflatoxin contamination exceeding 15 lg kg-1 makes the crop unsuitable for human consumption and, therefore, unprofitable. Irrigation to peanuts can be scheduled using soil moisture sensors, such as EnviroScan, Troxler Sentry (Leib et al. 2003), among others. However, their high costs, complex instrumentation, and the need for calibration for individual farms had limited their widespread use in the peanut industry. The soil temperature-based irrigation scheduling method ‘‘Irrigator-Pro’’ developed in the USA (Davidson and Williams 2002; Mahan et al. 2005), has not worked well for irrigation scheduling in peanuts in Australia, where ambient temperatures generally remain below the threshold temperature required for scheduling irrigations using this tool.
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There are other approaches, such as crop models, that have been attempted for scheduling irrigation in other areas. Crop models that simulate evapotranspiration demand at different stages of crop development have been found to assist in improving irrigation scheduling and have resulted in considerable water savings in peanuts (Santos et al. 2000; Guerra et al. 2005). From a grower’s viewpoint, the major problems hindering the use of a decision support system (DSS) based on crop modeling approaches appears to be the complexity of current models and the inability to access real time climatic data required to run the models (Fernandez and Trolinger 2007). In recent years, the internet, which has become a commonly available communication platform, has revolutionized software applications and data transfer. This has created the opportunity to develop simple online DSS’s that can allow input of daily climatic data and that can be easily upgraded without having to load new software versions onto a user’s computer (Fernandez and Trolinger 2007). However, this platform has yet to be fully utilized in streamlining agricultural operations. Internet-based irrigation DSS’s developed in the United States (Georgiev and Hoggenboom 1999) and Denmark (Jensen et al. 2000) seem to have been well adopted by growers. A number of other online DSS’s that facilitate irrigation scheduling have also been developed since 2001 (Leib et al. 2001; Thysen and Detlefsen 2006; Paz et al. 2007). The online DSS for irrigation scheduling developed by Thysen and Detlefsen (2006) was reportedly being used by 3% of Danish farmers. Another online DSS called AFLOMAN, which automatically downloads the latest climatic data and utilizes growers input of rainfall and temperature data, has been widely used by dryland growers to monitor inseason aflatoxin risk in peanuts in Australia (Chauhan et al. 2010). The success of AFLOMAN in predicting aflatoxin risk has been mainly due to its ability to predict changes in soil water content and soil temperature, which is done using the agricultural production systems SIMulator (APSIM) model (Keating et al. 2003). Such prediction in soil water content could also assist peanut growers in irrigation scheduling to improve overall irrigated peanut production. To meet the requirement of Australian irrigated peanut producers, an online DSS called AQUAMAN has been developed using the experience gained during the development of AFLOMAN. This paper presents a description of AQUAMAN and results of a series of activities (i.e., extension activities, case studies, and a survey) that were conducted to assess its level of acceptance among Australian peanut growers, obtain feedback for future improvements, and evaluate its performance.
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Materials and methods Description of AQUAMAN The approach used for irrigation scheduling in AQUAMAN combines the simplicity of the FAO-56 guidelines for scheduling irrigation (Allen et al. 1998) with the accuracy of the APSIM model (Keating et al. 2003) to predict crop water use. The APSIM peanut module used in AQUAMAN has previously been well validated for peanuts for different subtropical and tropical environments of Australia (Robertson et al. 2002). The APSIM model calculates crop transpiration as: T ¼ DDM=TE ð1Þ TE ¼ VPD=TEc
ð2Þ
DDMðpotentialÞ ¼ RUE LI
ð3Þ
DDMðtranspirationÞ ¼ soil water supply TE
ð4Þ
where, T = transpiration (mm d-1), DDM = daily dry matter production (g m-2), TE = transpiration efficiency (g m-2 mm-1 water), TEc = transpiration coefficient (kpa g-1 m-2 mm-1 water), VPD = vapor pressure deficit (kpa), RUE = radiation use efficiency (g MJ-1m-2) and LI = daily light interception (MJ m-2). The minimum DDM from Eqs. 3 and 4 is used in Eq. 1 to obtain T. Soil water demand is capped by the atmospheric evaporative demand and is adjusted by the proportion of green canopy cover and a crop factor. Soil water supply (mm) is determined as a function of available soil water and the fraction of roots in different layers. From FAO-56 (Allen et al. 1998), the following parameters for scheduling irrigation were derived: TAW ¼ 1000 ðhDUL hCLL Þ Zr
ð5Þ
RAW ¼ p TAW
ð6Þ
p ¼ 0:5 þ ½0:04 ð5 ETc Þ
ð7Þ
RZD ¼ 1000 ðhDUL hSW Þ Zr
ð8Þ
where, TAW = total available water in the effective root zone (mm), hDUL = volumetric water content at field capacity [m3 m-3], hCLL = volumetric water content at permanent wilting point [m3 m-3], Zr = effective rooting zone (maximum Zr = 0.5 m for irrigated peanuts), RAW = readily available water (mm) (refill point for irrigation scheduling), p = fraction of TAW that can be depleted from the crop root zone before crop water stress and reduction in crop evapotranspiration (ETc) occurs (0.1 B p B 0.8), ETc = crop evapotranspiration (mm day-1), RZD = root zone depletion (mm), hSW = volumetric soil water content (m3 m-3). ETc was calculated as the sum of APSIM-derived transpiration plus evaporation (mmd-1).
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Soon after a heavy rain or irrigation (when hSW C hDUL), the root zone depletion is zero (or even negative). The water content decreases as a result of percolation and evapotranspiration and results in an increase in the root zone depletion. In the absence of any wetting event, the water content steadily decreases until its maximum value (TAW) is reached (hSW = hCLL). To avoid crop water stress, irrigation is required when RAW is depleted (RAW B RZD). AQUAMAN determines hSW by conducting a daily soil water balance using the APSIM model. The days to irrigation (DTI) is then calculated as: DTI ¼ ðRAW RZDÞ=ETc7
ð9Þ
where, ETc7 = average ETc over the last 7 days. The crop needs irrigation when RZD reaches or is below the refill point line (RAW) (when DTI \ 1). To avoid deep percolation losses that may cause leaching of some essential nutrients out of the root zone, the net irrigation depth should not exceed RZD and the gross irrigation depth needs to consider the irrigation application efficiency, which varies depending on the irrigation system and the level of irrigation management. For sprinkler-irrigated peanut systems, the irrigation efficiency is typically about 80% (Harris 2002). The general architecture of AQUAMAN, including the web-based interface linking it to other necessary components, is shown in Fig. 2. AQUAMAN users access the system through the AQUAMAN website www.apsim.info/ aquaman. A link is established between the user and a cluster of 26 quad-core computers, which run APSIM via a ‘‘run-machine’’ computer maintained by the agricultural production systems research unit (APSRU) in Toowoomba, Australia. The AQUAMAN website provides general information about the software, how to use it, and how to create a user account. The website provides passwordprotected access to the AQUAMAN’s interface and allows growers to enter their farm agronomic details. The required inputs, some of which could be selected from the menu, are given in Table 1. The interface allows the user to input irrigation, rainfall and temperature, on a daily interval, by clicking the respective window button on the user’s page (Fig. 3). Alternatively, the ‘‘Enter all data’’ window allows growers to enter irrigation, rainfall, temperature (if recorded) by specifying the date and entering a value under the appropriate column (Fig. 4). In addition, the ‘‘observation’’ column of this window permits additional text or numerical inputs, such as the observed crop stage and pod yield. The interface saves the entered inputs to a Microsoft Access Database on the website by clicking the ‘‘Save’’ button. When the user requires an irrigation scheduling report, it is requested online by clicking the ‘‘Create report’’
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Fig. 2 AQUAMAN—a multi-component program. The arrows show the direction of information flow. An AQUAMAN user inputs field and management details, which the interface passes on to a runmachine computer whenever a report is requested. The run-machine computer generates APSIM run files for a cluster of quad-core computers. The cluster runs the APSIM software using climatic data input of the user and the SILO data base, and returns the output to the interface via the run-machine computer
button. When a report is requested, the farm-specific information is e-mailed from the web interface to the runmachine computer. The computer sends this information to a cluster, which patches the farm-specific parameters into an APSIM simulation template for the generation of the required information. The required radiation and temperature data for the current season is automatically downloaded from the database of climatic information maintained at the SILO (www.longpaddock.qld.gov.au/silo) website. The user’s inputs, including temperature, rainfall and irrigation, and the APSIM template are sent to the run-machine, which triggers the run on the computer cluster and the output is returned to the run-machine computer. The outputs from the computer simulation are automatically graphed by the APSIM reporting software (APSIM Report) and a report is produced as a GIF file. This report is then sent to the AQUAMAN website for the user to view or download. When the requested report is ready, AQUAMAN automatically sends an e-mail to the user. The whole process, from opening the website to receiving a report, is typically completed within 5–15 min.
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Table 1 Required starting and in-season inputs by AQUAMAN Starting inputa
In-season daily input
Field
Management
User
Soil type*
Sowing date*
Maximum temp.
Meteorological station (nearest)* Maximum temp.
Starting water*
Variety*
Minimum temp.
Minimum temp.
Rain*
Radiation
Irrigation* a
Selectable from the menu; *mandatory input. Maximum and minimum temperatures if provided by the user were patched over the data obtained from the nearest weather station
Fig. 3 A screen shot of window of a typical grower’s page with field details when growers logged into their AQUAMAN account
Fig. 4 A screen shot of the ‘‘enter all data’’ window to enter maximum and minimum air and soil temperatures, rainfall, and irrigation
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AQUAMAN adoption and performance A series of activities, including extension activities, case studies, and a survey, were conducted to assess the level of acceptance of AQUAMAN among Australian peanut growers, obtain feedback for future improvements, and evaluate its performance in commercial peanut farms. Following is a description of these activities. Extension activities The first version of AQUAMAN was released during the 2004–2005 season and extension activities to promote its adoption and assess the level of acceptance among peanut growers were conducted soon after by the development team and collaborating crop consultants. The team trained a group of crop consultants, which were later contracted to promote adoption, train growers and gather feedback from users. The development team and consultants gave a number of presentations at growers’ meetings, which further enhanced growers’ awareness and served as a way of obtaining feedback. Individual training sessions, usually lasting about 30 min, were also held for interested growers. Technical support was also given via telephone and e-mail. The program was regularly updated based on the feedback received. Case studies A number of growers have been using AQUAMAN for irrigation scheduling of commercial peanut crops since it was released in the 2004–2005 season. The success of the adoption process and usefulness of AQUAMAN was Table 2 Total irrigation, rain, potential evapotranspiration (Pot. ET), seasonal evapotranspiration (ETc), pan evaporation (Pan evap), and observed (pay weights) and predicted pod yield, water use efficiency (WUE = yield/ETc), and irrigation water use efficiency Sowing date
Irrigation (ML ha-1)
evaluated in case studies involving seven farms in Bundaberg, which were spread over five growing seasons between October 2004 and May 2009 (Table 2). Five of these farms were irrigated based on the scheduling information obtained from AQUAMAN and the other two following the common grower’s judgement, which roughly matched the pan evaporation demand during rain-free periods (Table 2). All farms involved were gently slopping or have been laser-leveled for surface irrigation. They were either naturally well drained or had subsurface drainage systems to remove excess water. Field size varied from 3 to 50 ha in different seasons. The soils of these farms were classified as chromosols, which hold about 88 mm of plant available water in the top 1.9 m soil profile. Following local recommendations, lime (1 t ha-1) was applied to increase calcium availability in the podding zone. All farm operations from sowing to harvesting were mechanized and managed by the growers. Approximately 15 seeds m-2 of the high oleic runner-type peanut variety ‘‘Holt’’, which requires about 2,380°C days to mature, were planted to well-tilled fields following a sugarcane crop. Sowings were performed on a 0.9 m row spacing on 1.8-m wide beds and the furrows of these beds served to provide machine traffic and to drain excess water. Crops were protected from weeds using herbicides, and rust and leaf spot diseases were controlled using cyproconazole fungicides applied at about 14 days intervals from the first sign of foliar diseases according to local best management practice (Crosthwaite 1994). Peanut crops in each of these fields exhibited a good degree of within-field uniformity. Fields were irrigated using travelling irrigators (in 2004–2005 and 2005–2006), winches (in 2006–2007 and 2007–2008), and overhead sprinklers (in 2008–2009). (IWUE = yield/Irrigation) of peanut cultivar Holt in five AQUAMAN case studies conducted in the Bundaberg District and farmer practice in 2008/2009
Rain (mm)
Pot. ET (mm)
Pan evap. (mm)
ETc (mm)
Pod yield (t ha-1) Obs.
Pred.
Meana
1,013
593
6.5
7.0
WUE (kg m-3)
IWUE (kg m-3)
5.0
1.1
5.9 2.1
AQUAMAN irrigation 28 Nov. 04
1.1
605
805
28 Nov. 05
3.5
558
795
985
613
7.2
8.1
5.7
1.2
14 Oct. 06
2.5
338
849
1,088
636
7.0
7.0
4.2
1.1
2.8
28 Oct. 07
2.7
590
734
903
610
7.0
7.1
4.9
1.1
2.6
23 Oct. 08
1.7
467
801
1,005
597
6.7
6.5
5.0
1.1
3.9
Mean
2.3
512
797
999
610
6.9
7.1
5.0
1.1
3.5
Farmer practice
a
22 Sep. 06
4.8
307
802
1,012
605
5.39
ND
4.2
0.9
1.1
3 Oct. 08
1.9
585
887
986
597
4.78
ND
5.0
0.8
2.6
Mean pod yield of all Bundaberg peanut growers; ND = not predicted due the lack of information on irrigation dates
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Irrigations largely followed AQUAMAN recommendations, but in a few cases growers chose to slightly alter the schedule depending on their individual circumstances. Rainfall and irrigation depths were monitored by the growers using rain gauges or catch cans. To measure the amount of irrigation and rain gauges or catch cans (at least one per 25 ha) were placed at about 15 m from tow paths so that winches walked past during lowest wind times, usually during the nights to increase uniformity of application. Daily rain and irrigation depths were recorded and entered into AQUAMAN on the dates these events occurred; the other dates were left blank, which expedited data entry. Daily maximum and minimum air temperatures were recorded using data loggers, which were read by growers and entered into AQUAMAN. Recording of temperatures by growers was discontinued after the 2006–2007 season, as it was found to be too time consuming to collect and input the data into the computer. Solar radiation and temperature data (when not entered by growers) were obtained from the nearest meteorological station through the SILO website. The amount of water applied by growers in 2006–2007 and 2008–2009 to match pan evaporation demand (henceforth referred to as farmer practice) was obtained from the growers. All the fields were machine-harvested and threshed. The observed pod yield was measured at the end of the season, which occurred when crop accumulated about 2,380°C day. Two of the charts in AQUAMAN reports indicated daily thermal time accumulation, and the stages of crop development including maturity. The predicted crop growth stage was confirmed by field sampling of pods. A crop was considered fully mature when over 80% of the pods had the blackened inner side of the shell wall (Mackson et al. 2001). Pod yield was calculated by dividing the pay weights that growers delivered at buying points by the sizes of their respective fields. The pay weight excluded commercially unrecoverable pods left in the ground and small unfilled pods. As the pods were free from any aflatoxin at the time of delivery at the buying points, they were classified as a ‘‘Seg 1’’ product by the sheller, which is the best grade for maximum payment. The pod yield of Holt peanuts of the two growers not irrigating using AQUAMAN in 2006–2007 and 2008–2009 was also recorded, as described above. The predicted pod yield and estimates of seasonal ETc were obtained by separately running the APSIM model at the end of each season using the same dataset as used for scheduling irrigation by growers, as AQUAMAN reports did not show pod yield. For this, AQUAMAN had a feature to generate APSIM control and parameter files that were e-mailed to its administrators for additional analyses, if so desired. Rainfall and irrigation data and other agronomic
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information were obtained with the growers’ permission from their AQUAMAN web accounts. The average yield reported was calculated by dividing the total peanuts received at the intake point by the total area of peanuts. To obtain information on the depth of soil water extraction, soil water in one of the AQUAMAN irrigated farms was monitored during the 2006–2007 season using a Sentek Diviner 2000 (Sentek Environmental Technologies, Australia). Measurements were taken several times during the growing season from three Deviner Tubes (1 m deep) installed at a distance of 100 m from each other in representative areas of the crop. Water use efficiency (WUE; kg m-3) and irrigation water use efficiency (IWUE; kg m-3) for the case study sites were calculated as described by Payero et al. (2008). WUE ¼ Y=ETc
ð10Þ
IWUE ¼ Y=I
ð11Þ
where, Y = observed pod yield (g m-2), ETc = model estimated seasonal crop evapotranspiration (mm), I = seasonal irrigation (mm). Survey An ‘‘on-the-spot’’ survey involving twelve peanut growers and consultants, which represented about one-third of the total peanut growers in the Bundaberg region, was undertaken in August 2008 to determine awareness, level of acceptance and obtain further feedback on its performance. The survey sought information on nine aspects, as outlined in Table 3. The survey was conducted in conjunction with a peanut update with farmers, which included some old as well as prospective peanut farmers in the Bundaberg region.
Results Results of case studies Irrigation application The results of the case studies conducted on AQUAMAN irrigated farms indicated that the amount of irrigation applied varied from 1.1 ML ha-1 (1 ML ha-1 = 100 mm) in 2004–2005 to 3.5 ML ha-1 in 2005–2006 (Table 2). Growers applied irrigations to keep the root zone depletion above the refill line (RAW) based on AQUAMAN reports generated throughout the growing season. There was no relationship between the total amount of irrigation applied and in-season rainfall, which was the result of normal variability in rainfall amount and timing.
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278 Table 3 AQUAMAN survey and the range of responses from 12 growers and consultants
a
The number in parenthesis refers to the number of respondents for that response
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Survey question
Range of answers
Current practice of scheduling irrigation
Growers(7)/consultants’ judgement(1)a/Sensors (4)
Awareness about AQUAMAN
Yes (10, used 7) and No (2)
Source of information about AQUAMAN
Consultants (9), Update (2), and the DEEDI Website (3)
Water saving from AQUAMAN
Yes (6)
Yield increase from AQUAMAN
Yes (4) and Don’t know (2)
Experience in using AQUAMAN on the internet
Easy (5), and Difficult (3)
Further interest in using AQUAMAN
Yes (6), and No (1)
Training requirement for AQUAMAN
Yes (10)
Suggestions to improve AQUAMAN
Increase reliability of the service (1), Increase cropping options (1), add a diary for spray scheduling (1), reduce time taken for gathering climatic data (1) and report generation (1), make more dial-up friendly (1)
The irrigation scheduling report presented in Fig. 5 provides an example of the daily soil water dynamics in one of the AQUAMAN irrigated farms during the 2006–2007 seasons. For this particular farm, the total in-season rainfall was 338 mm with 10 irrigations applied of 19–29 mm for a total irrigation depth of 250 mm (2.5 ML ha-1). This ensured that the crop did not suffer from stress, especially after the commencement of flowering. The report also indicated progress of heat unit accumulation and the stage of crop development, percent ground cover, predicted days to next irrigation and the required application depth. In 2006–2007, growers using the farmer practice applied 4.8 ML ha-1 in nine irrigation events and received 307 mm (3.07 ML ha-1) of rainfall (in-season). In 2008–2009, they applied 1.85 ML ha-1 in five irrigation events and received 585 mm (5.85 ML ha-1) of rainfall (Table 2). In 2008–2009, the irrigation applied in the AQUAMAN irrigated farm was 1.7 ML ha-1 in six irrigations and received 467 mm (4.67 ML ha-1) of rainfall (Table 2). As indicated by the actual diviner soil moisture data obtained in an AQUAMAN irrigated farm, most of the moisture was extracted from the top 30 cm soil layer (Fig. 6). Crop performance The observed pod yield in the AQUAMAN irrigated farms varied with season between 6.5 and 7.2 t ha-1, which was about 3% less than that predicted by the APSIM model (Table 2). The average commercial pod yield in these seasons in the Bundaberg region was approximately 5 t ha-1. The maximum predicted pod yield was 8.1 t ha-1 for the 2005–2006 season. The observed pod yield in these AQUAMAN irrigated farms was positively (R2 = 0.98, n = 5)
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related to the irrigation depth, even though the irrigation was given by different growers using AQUAMAN. The observed pod yield in the fields irrigated following farmer practice was 5.4 t ha-1 in 2006–2007 and 4.8 t ha-1 in 2008–2009. The crop in the AQUAMAN irrigated farm, whose report is shown in Fig. 5, was simulated to have reached maturity in 160 days, which closely matched to that determined by the shell-out method (Mackson et al. 2001). Pod yield obtained from this farm was 7.0 t ha-1 (Table 2). Water and irrigation use efficiencies The WUE of AQUAMAN irrigated farms averaged 1.1 kg m-3 over 5 years with inter-annual variation being quite small. WUE of the nearby farmer fields was between 0.8 and 0.9 kg m-3 (Table 2). In AQUAMAN irrigated farms, IWUE varied from 2.1 to 5.4 kg m-3, decreasing with irrigation depth (425.61* x-0.911; R2 = 0.99; where x = irrigation in mm). The average IWUE was 3.5 kg m-3. The average pan evaporation during this period was 999 mm and the difference between pan evaporation and rainfall being 487 mm or 4.87 ML ha-1. The average irrigation applied was 2.3 ML ha-1, about 47% of the difference between rainfall and pan evaporation. IWUE of the famer’s field was 1.1 kg m-3 in the 2006–2007 and 2.6 kg m-3 in 2008–2009. The comparative IWUE realized using AQUAMAN was 2.8 kg m-3 in 2006–2007 and 3.9 kg m-3 2008–2009. Results of survey Results of the survey showed that of the twelve growers and consultants surveyed, seven had used AQUAMAN, and one out of six was still not aware of AQUAMAN
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Fig. 5 A sample AQUAMAN report generated by a peanut grower in the Bundaberg District during the 2006/2007 peanut growing season. The top charts indicate thermal time accumulation (top left), percent ground cover (top right), different stages of crop development (middle left), suggested water requirement, and the amount of water applied (middle right). The lower-most chart was used for irrigation scheduling and shows root zone water depletion, rainfall, and irrigation applied. The text just above this chart gave approximate days to next irrigation and daily water use
(Table 3). The range of practices that growers used for irrigation scheduling included their own judgement, which was generally based on appearance of crop wilting symptoms, consultants’ advice, water sensors, and other methods such as whether or not soil formed a ball on the palm of the hand when rolled (hand-feel method). Growers recognized that industry consultants played a key role in promoting AQUAMAN. All the growers expressed interest in using the program and in receiving training. While no clear indication of yield advantage was available from growers, a peanut consultant indicated a yield advantage of up to 2.5 t ha-1 by using AQUAMAN. A few growers mentioned the need to make the website more reliable and to reduce the time needed by the system to generate a report.
Discussion Performance of AQUAMAN Irrigation management using AQUAMAN is a relatively new concept for peanut growers in Australia. The tool has been developed to assist those growers who relied on visual crop observation to better schedule irrigations. AQUAMAN is intended to remove guesswork in irrigation by providing more realistic estimates of crop water demand in different environments, and assist those who are unable to get timely advice from crop consultants. AQUAMAN is able to do this without the need for dedicated expensive irrigation scheduling equipment based on soil probes. In
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Fig. 6 Soil water content at different soil depths (soil depths given in line legends are in centimeters) measured with diviner tubes in a field irrigated following AQUAMAN (data shown are the average of readings from three diviner tubes)
addition, internet access makes it easy to use, allowing input of specific field characteristics, rainfall and irrigation, which are essential for irrigation scheduling using a crop model. Combining AQUAMAN with well-accepted FAO irrigation guidelines could help optimize irrigation use and its efficiency. The use of AQUAMAN will also make it easier for crop consultants to monitor crops for several growers by keeping track of field moisture status and stages of crop development without having to directly monitor soil moisture in the field. The AQUAMAN user interface keeps the APSIM modeling complexity in the background, which is suggested as a key requirement for a successful online DSS (Fernandez and Trolinger 2007). The best practice in scheduling irrigation, as per FAO56 guidelines, is to meet the crop evapotranspiration demand during rain-free periods and the trigger point for applying irrigation is to be determined using a water balance approach (Allen et al. 1998). As peanut growers in Australia do not regularly measure evapotranspiration, they usually apply around 45–60 mm each week to match the pan evaporation rate of about 7 mm day-1, generally after the crop has started to wilt in the field. This practice has been reported to lead to a 20% reduction in yield compared to more frequent irrigations that would prevent crop stress (Crosthwaite 1994). With AQUAMAN, growers do not have to wait for crops to show symptoms of stress. The increase in pod yield in AQUAMAN irrigated farms reported in this study is not surprising. In all five case studies, growers obtained more than 6.5 t ha-1 and the average pod yield was 6.9 t ha-1 over 5 years, which was about 38% more than the overall commercial average yield of irrigated peanuts of about 5.0 t ha-1 recorded in the Bundaberg region. In 2006–2007 and 2008–2009, pod
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yield of growers irrigating using the farmer practice was similar to the average yield in the Bundaberg region. Thus, AQUAMAN growers were more likely to derive greater economic benefit from the increase in pod yield alone. However, there are other potential benefits arising from the optimal management of irrigation, including the effective control of aflatoxin (Craufurd et al. 2006), control of pod damaging insects like Etiella (Etiella behrii) and white grub (Coleoptera:Scarabaeidae), which thrive under drier soil conditions (Crosthwaite 1994), and improvements in seed size, grade and $/t (Zhu et al. 2004). These benefits, however, were not quantified in this study. AQUAMAN can also help growers reduce the ‘‘yield gap’’ between the observed yield and attainable yield (Wright et al. 2001). In our study, the observed pod yields in the AQUAMAN irrigated farms were only slightly (3%) less than predicted, as these excluded a fraction of lost pods (not quantified), which invariably occurs during machine digging, threshing, and removal of smaller pods. These pod losses are not currently modeled by APSIM. Santos et al. (2000) in a study in France and Portugal scheduling irrigations based on the EPIC model reported savings of two irrigations. In our study, however, AQUAMAN did not reduce the total number of irrigations compared to the farmer practice, but reduced irrigation depth by about 50% in the 2006–2007 relatively dry season. This saving was mainly due to the application of smaller amounts of water per irrigation. The difference in irrigation depth between the two practices was, however, relatively small in the wetter 2008–2009 season, which was to be expected. The increase in the number of irrigations could be because growers using AQUAMAN would have irrigated their crops without waiting for wilt symptoms to appear, thus resulting in slightly earlier application after the last rain or irrigation event compared to the farmer practice. AQUAMAN predicted more frequent irrigations, which prevented plants from needing to extract water from soil layers deeper than 50 cm (Fig. 6), as recommended in the FAO guidelines (Allen et al. 1998), and thus spent less energy developing deeper root systems to exploit water from deeper layers. Wright et al. (1991) showed that timing of stress had important implications for yield and water use efficiency in peanuts. In our study, growers using AQUAMAN realized considerably higher WUE and IWUE than those who used the farmer practice. Irrigation applied following AQUAMAN not only ensured more timely applications but also reduced runoff and deep drainage losses due to application of smaller irrigation depths. Since such efficiency gains could be larger in lower rainfall years, when pressure on irrigation resources would be greater, the use of such tools may be very helpful in ensuring judicious use of scarce water and increasing profits.
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For a subterranean crop such as peanut, accurate assessment of maturity is very important to determine when to terminate irrigation applications and prevent pods from sprouting in the field and becoming detached from the plants, thus ensuring better overall quality. A delayed harvest of peanuts has been found to result in substantial increase in yield losses (due to weakening of pegs) and reduction in gross margins (Young et al. 1982). A number of approaches to assess peanut maturity have been reported (Shorter and Simpson 1987; Mackson et al. 2001), but they require destructively sampling of a representative number of pods to assess maturity. With its maturity prediction ability (Fig. 5), AQUAMAN is able to limit irrigation during the growing period, ensuring a timely harvest. AQUAMAN was also used by growers in south Queensland locations of Texas and the Tableland regions in north Queensland (results not reported here). In Texas, where the pod yield potential is even higher (Wright et al. 2001), a greater yield advantage and greater adoption of AQUAMAN was expected. However, as irrigation in this region is continuously applied throughout the season using center pivots and water is limited and insufficient to meet crop water demand, growers saw little advantage in scheduling irrigation. In north Queensland, which has more reliable rainfall compared to other Queensland regions, the adoption of the DSS was found to increase in drier years when some benefits of AQUAMAN were apparent. In 2008–2009, over twenty growers used the program to various extents, including the use of AQUAMAN by a north Queensland grower who produces peanut crops for winter seed production. Training given to consultants and growers has played a key role in increasing the adoption of AQUAMAN. Even though it takes only a few minutes to input information and generate a report, a few growers still found these tasks a bit daunting and preferred to delegate them to other family members or to a crop consultant. Adoption of AQUAMAN The results of the survey suggested that growers who used AQUAMAN seemed satisfied with the information it provides to help them make irrigation decisions. Another indicator of their satisfaction was their continued usage in succeeding years. New peanut growers were also receptive, particularly when they were not familiar with the irrigation requirements of peanuts. They liked the feature of AQUAMAN that predicted the number of days until irrigation, particularly during busy periods when they were focused on other issues such as machinery maintenance and breakdowns. There was a tendency among irrigated peanut growers in the region, some of whom were also sugarcane growers, to delay irrigations until the last minute,
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especially after good rainfall events, in order to complete other tasks. AQUAMAN advised them to start planning for irrigation in advance. The retention of electronic records on the internet provided by AQUAMAN also meant there was no need for the user to keep paper records of irrigation scheduling inputs and outputs. In the Bundaberg region, where growers also grew a number of other crops, the use of AQUAMAN was limited because it was only applicable for peanuts and not for other crops. There could be wider adoption of this tool if this could be extended to other crops that were important in the region, as growers dislike the idea of using different DSS’s for different crops. Thus, development of a whole-farm irrigation scheduler is definitely a desirable longer term objective. APSIM can now simulate crop growth and development of 22 crops and with a little bit of modification in the AQUAMAN interface it should be feasible to schedule irrigation in all those crops. It would save the need for growers to learn different DSS’s and eventually provide greater confidence in the approach. Lack of faster broadband and instability experienced in using dial-up internet connectivity also contributed to limited adoption of AQUAMAN in some areas. In a few cases, this DSS tool gave an additional reason for growers to subscribe to faster broadband connectivity. The adoption of AQUAMAN could be enhanced through support from crop consultants, since local peanut growers generally rely on them for technical advice for managing their crops. The consultants could operate AQUAMAN and charge a fee to their client growers. However, the requirement of a computer being available to them all the time could still be a limitation. As new smart phones now allow web access, it should be possible to increase the uptake of such DSS’s by incorporating smart phone compatibility. AQUAMAN is one of the many online DSS’s for irrigation scheduling currently operating in Australia, but it is the only one specifically targeting peanuts. However, AQUAMAN seems to be the only DSS that has integrated the FAO-56 guidelines with a simulation modelling approach. Other available irrigation DSS’s include WaterSense, which was developed in Australia for irrigation scheduling of sugarcane and uses a water balance approach, but has a different interface (Inman-Bamber et al. 2005). Another online system called YieldProphetÒ, was developed for nitrogen and irrigation management of wheat and barley (Hochman et al. 2009) and has been in operation since 2003–2004. Similarly, AspireNZ was develop in New Zeland for asparagus (Asparagus officinalis L.) (Wilson et al. 2001). The above examples suggest that internet-based DSS’s are now becoming popular since they have the ability to assist growers by providing concise and relevant information for specific locations and in near
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real time. Because of this they seem to have better acceptance than previous stand-alone software packages.
Conclusions AQUAMAN has been successfully used to enhance irrigation management of commercial peanut crops since the 2004–2005 growing season. It made effective use of the FAO-56 guidelines in combination with the APSIM peanut model in assisting irrigation management in peanuts in an easy to use format. Several case studies using AQUAMAN resulted in higher pod yield with less irrigation, consequently increasing WUE and IWUE. A survey of farmers using AQUAMAN provided positive feedback and recommendations for further work, which are currently being implemented. Currently, the DSS has been mainly focused on assisting peanut growers to schedule irrigation. However, a similar approach could be relatively easily applied for other crops. Improved broadband connectivity may enhance its adoption and future addition of smart phone compatibility could further add to the flexibility in its use. Acknowledgments We would like to acknowledge the contribution of: (a) the Grains Research and Development Corporation (GRDC) for funding the development of AQUAMAN through the projects DAQ00091 and DAQ00123, (b) peanut consultants (Peter Hatfield, Ian Crosthwaite, Duane Evans, Patrick Jones and Tony Crowley) and staff from the Peanut Company of Australia (Pat Harden and Grant Baker) for their input in the extension of AQUAMAN to growers, (c) all growers who tested AQUAMAN, and (d) Bundaberg Sugar for sharing information on commercial pod yield and irrigation data for the 2006–2007 and 2008–2009 seasons.
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