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Irrigation automation with heterogeneous vegetation: the case of the Padova botanical garden
Agricultural Water Management 55 (2002) 183±201
Irrigation automation with heterogeneous vegetation: the case of the Padova botanical garden Francesco Morari*, Luigi Giardini Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Agripolis, UniversitaÁ di Padova, via Romea 16, 35020 Legnaro, Padua, Italy Accepted 10 December 2001
Abstract An automated control system was set up in the Padova Botanical Garden integrating the information on the soil water status supplied by time domain transmissometry (TDT) sensors with the aid of an irrigation microcomputer. The automatic system consisted of four parts: (a) an irrigation network and electronic control unit (microcomputer); (b) a monitoring system of the soil moisture and water table depth; (c) a management software; and (d) a datalogger connecting sensors to the irrigation microcomputer. Sensors were chosen so that they could be remotely connected through cabling over more than 100 m, guaranteeing adequate accuracy and high reliability over time. To take into account the heterogeneity of plant cover, the site was divided into six irrigation macro-sectors managed separately by the automated system. These macro-sectors were selected by classifying the area on the basis of water requirements, cover type and evapotranspiration demand. The software allowed different irrigation criteria to be de®ned, considering the values supplied by the moisture sensors singly or on average. In the ®rst year, the automation worked adequately, allowing the irrigation to be managed on the basis of de®ned thresholds. The irrigation criterion used in the ®rst year for the automated management within macro-sectors, although allowing a favourable water potential to be maintained on average, does not appear to have adequately evaluated the variability of behaviour of the different plants. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Irrigation; Automation; Monitoring system; Geographical information system; Heterogeneous vegetation; Botanical garden
* Corresponding author. Tel.: 39-049-8272857; fax: 39-049-827-2839. E-mail address: [email protected] (F. Morari).
0378-3774/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 1 ) 0 0 1 9 2 - 5
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1. Introduction Different criteria can be used to identify the timing of irrigation (Stegman, 1983). Those based on the soil water status, monitored using electronic sensors and/or simulated by water balance models, currently offer the best application possibilities. These allow the irrigation to be fully automated, using automated data acquisition methods to integrate information from weather stations and the electronic sensors with the irrigation control systems (Howell, 1996). Research in the ®eld of automation (e.g. Cary and Fisher, 1983; Phene and Howell, 1984; Roberson et al., 1996; Abraham et al., 2000) has not only focussed on the criteria for determining irrigation times and volumes, but also on control systems and sensor technology. In the latter ®eld, the range of electronic sensors has been extended, in particular of those that measure the dielectric properties of the soil, such as the time domain re¯ectometry (TDR) sensors, frequency domain re¯ectometry (FDR) sensors, capacitance sensors, etc. There are various problems at this level, from sensor calibration to spatial sampling, from processing and interpreting the signals to verifying the reliability of the instrumentation over time and in ®eld conditions, etc. In the past, research has mainly involved homogeneous cropping conditions, while a case study on irrigation automation in the presence of heterogeneous vegetation, such as in parks and gardens, is lacking. In these heterogeneous areas automation is usually entrusted to electronic control units that activate the opening and closing of the electromagnetic valves at pre-set times and hours. Irrigation is therefore done at ®xed intervals without directly taking the evapotranspiration requirement, rainfall and soil water content into consideration. This creates highly inef®cient situations when, e.g. soil previously saturated by rainfall is irrigated, or when the irrigation volume is no longer able to satisfy increased evapotranspiration demand. The irrigation microcomputer producers try to partly avoid the problem of excess water by connecting the electronic control units to a sensor capable of terminating the irrigation when the rainfall (rain sensor) or soil water content (soil moisture sensor) exceeds a ®xed threshold. These devices can also be inef®cient: in the ®rst case because the rainfall threshold cannot be determined exactly in advance, as it varies depending on the characteristics of the rainfall event (e.g. intensity), previous soil moisture content, evapotranspiration demand, etc.; in the second, because a single sensor cannot represent the soil water status in a non-uniform area like a park or garden. This type of area has a strong spatial variability, arising from variability in soil hydrological properties, nonuniform irrigation, soil surface pro®le and, lastly, from the different absorption pattern by roots and varying evapotranspiration demand. The question of irrigation automation in an area with heterogeneous vegetation was posed at Padova Botanical Garden when setting up a new irrigation system (Giardini and Morari, 2000). The garden (Hortus botanicus patavinus) was founded in 1545, by a decree of the Venetian Republic, to create a place for the display, study of and experimenting with medicinal plants or ``simples''. It is the oldest botanical garden in the world still maintaining its original location, ground-plan and functions. Its historical, cultural and architectural importance is recognised throughout the world and, in 1997, it was included in the UNESCO ``World Heritage'' list of monuments. Following, it was included in the list of ``100 most endangered sites'' compiled by the World Monuments Fund, as intensive
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building operations close to the perimeter of the garden in 1996 had lowered the water table, altering a long-established equilibrium between groundwater and vegetation (Ricceri and Simonini, 1998, personal communication). This prompted the University of Padova, with the collaboration of some private sponsors, to set up an automated irrigation system to prevent any risks of water stress. This paper presents: (a) the automated irrigation system of the Padova Botanical Garden; (b) the technical aspects related to soil water monitoring and, in particular, the spatial sampling issues; (c) the performances of irrigation control and problems encountered in the ®rst experimental year. 2. Materials and methods 2.1. The site The botanical garden extends over a trapezoidal area of 2 ha bounded in the north and west by the Alicorno canal (Fig. 1). Because of its position within the city, the site suffers from the effects that urbanisation, traf®c and industries have on the climate. The climate is sub-humid, with an average annual rainfall of 805 mm. Because of the ``heat island'' effect the average annual temperature is 0.8 8C higher than the rural environment of the Faculty of Agriculture, located 10 km outside of Padova. The buildings that surround the garden act as windbreaks, slowing the speed and altering the main wind directions. Moreover, due to pollution and the shadowing effects of trees and buildings, solar radiation is up to 10% lower than in the rural environment. The FAO-UNESCO classi®cation of the soil is Calcari Urbic Anthrosol (Atuc), silty loam. The low porosity of the soil and its high resistance hinder root growth beneath 60± 70 cm from the soil surface. An impervious clay layer at a depth of 3.50 m gives rise to a water table that ranges in depth between 60 cm during autumn±winter and 160 cm in the summer on average, but with wide ¯uctuations depending on the weather (Giardini and Morari, 2000). Around 2300 species are cultivated in the garden (excluding those in the greenhouses), assembled in collections on the basis of their systematic or ecological characteristics, utilisation or place of origin. In general, the herbaceous collections are in the central area (Hortus cinctus), while the tree ones are around this. The latter include a collection begun in 1700 that has now assumed the characteristics of a small wood (Arboretum) (Fig. 1). 2.2. Automation of the botanical garden irrigation system A plan of the automation system is given in Fig. 2. The system can be divided in four parts: (a) Irrigation network and electronic control unit (microcomputer): The irrigation system consists of about 550 sprinklers controlled by 27 solenoid electromagnetic valves. An electronic control unit that activates the solenoids by means of a 24 V relay manages these valves. The electronic control unit allows the programming of the sequence and
Fig. 1. Map of Botanical Garden of Padova.
Fig. 2. Layout of the automation system.
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times of opening (on) and closing (off) of the valves. It is thus possible to irrigate 27 single sectors with different cycles and volumes. (b) Monitoring system of soil moisture and water table: A series of 15 TDT moisture sensors (Gro-PointTM; ESI, 1999a) and 10 electronic piezometers are scanned and acquired by a datalogger (CR10X, Campbell Scientific) with a 16 differential channel multiplexer (AM416 Campbell Scientific). The 10 piezometers, placed in pairs at two depths (3 and 6 m), are of the open tube type with porous filtering cell; a pressure transducer connected to the datalogger is placed inside the tubes. (c) Management software and workstation: The software, programmed in Visual C, allows different functions to be performed: (1) datalogger functions (definition of the scanning and registration intervals, definition of the automatic reset in the case of central processing unit (CPU) power failure, etc.; (2) instant display on video of the measurements; (3) formulation of the irrigation schedule through the definition of the thresholds and criteria; and (4) downloading, recording and display of the data contained in the datalogger memory. (d) Datalogger connecting unit to the irrigation microcomputer: This represents the core of the automated system. The go-ahead for an irrigation is given by the datalogger that, on the basis of the defined thresholds and criteria, open the connecting circuit between the irrigation microcomputer and the electromagnetic valves through a 12 V relay. The datalogger is equipped with six switches in rotation and is therefore able to automate the irrigation differently in six macro-sectors, composed of one or more sector. The core of the automation software is loaded in the datalogger and not in the CPU, a choice allowing the system to work under adverse environmental conditions, such as sudden changes in tension or prolonged blackouts. The contacts of the relays are normally closed, a position that means the time-dependent automation can work when the datalogger is disconnected. 2.3. The TDT probes The TDT method comes within the wider category of time domain methods. These methods exploit the dielectric behaviour of the soil, which varies as a consequence of changes in water content (Topp et al., 1980). Time domain methods measure the time a fast pulse edge takes to travel along a section of transmission line buried in the soil. Unlike TDR, where the time is measured after the signal has been re¯ectedÐi.e. the signal covers the length of the wave guide twice, in TDT the time is measured at the other end of the transmission line, after the impulse has covered the wave guide only one way (ESI, 1999a). Although the accuracy of the TDR method is better than the TDT method, the latter offers important advantages for irrigation automation. The sensor output (5±50 mA) is simple to analyse and cabling gives remote connections up to 300 m. This aspect is of fundamental importance in the garden where the distance between the datalogger and the sensors is more than 100 m in some cases, and its structural simplicity guarantees robustness and long-life (ESI, 1999a). The level of accuracy measured in controlled laboratory conditions is, for the majority of soils (including that of the garden) 0.03 mm mm 1 (ESI, 1999a). The volume of soil monitored by the probes is 150 mm 230 mm 370 mm.
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2.4. Criteria for positioning the probes The criteria for de®ning the number and position of the probes are a compromise between the need to represent the variability observed in the moisture values in the three dimensions x, y and z and the technical and economic constraints involved in setting up the automated system. In addition, there is the problem of the uniqueness of the place, under the protection of UNESCO, that limits digging operations for positioning the sensors and connecting cables. For these reasons the number of probes was limited to 15, a number that could, after an initial experimental phase, be slightly increased in the future. As a general criterion, when positioning the sensors it was preferred to represent the surface variability (dimensions x, y) as well as possible, rather than the variability observed at depth (dimension z). The probes were therefore placed in an inclined position so as to give integrated measurement of the moisture in the root layer, i.e. between 5±10 and 35± 40 cm. In order to take into account the dynamics of the water below this depth, probes were also placed in the 40±70 cm layer. A vertical or inclined probe can have lower accuracy because of errors linked to the non-linear integration of pro®les with different moisture contents (Woodhead, 1996). However, these errors are in the order of 1% and can therefore be considered acceptable for this application. The advantage is the possibility of obtaining an average depth measurement of soil moisture content with just one sensor. The inclined position was preferred to the vertical one to limit the phenomena of preferential ¯ow (Zegelin et al., 1992). Although it would be possible to automate the irrigation using just one sensor placed horizontally (Campbell and Campbell, 1982), recent studies with FDR sensors have demonstrated the imprecision of the method in determining the exact instant in which to terminate the irrigation (Lukangu et al., 1999). These authors therefore suggest monitoring the moisture throughout the root pro®le. Positioning the probes on the surface is more of a problem as the high species variability must be integrated with a limited number of probes and the current limits in managing the go-ahead for irrigation, which can only be differentiated into six macro-sectors. The ideal automatic irrigation management would, in fact, necessitate macro-sectors (a) composed of individual plants with the same water requirements and cover type (ground cover, height, roughness, etc.) and (b) with the same evapotranspiration demand. Hypothesising adequate hydrological uniformity, these characteristics would allow common intervention thresholds to be de®ned and to have the same soil moisture dynamics. Being purely theoretical, it would be dif®cult to ®nd these conditions in the garden. They anyway represent a criterion for selecting suf®ciently the homogeneous macro-sectors for automated management. De®ning irrigation classes according to the earlier-mentioned parameters (water requirements, cover type and evapotranspiration demand) allowed the sectors to be identi®ed. A parametric method based on the classi®cation proposed by Landolt (1977) was used to classify water requirements. According to this classi®cation the requirements of a species are evaluated subjectively, attributing a merit score from 1 to 5: the value increases as the requirements rise. Landolt's classi®cation refers to the ecological requirements that do not simply express the physiological possibilities of the species. When competition is limited, or absent, the requirement might differ from that indicated by the index. Despite this limitation the system was adopted because: (a) no information is available in the literature
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on the physiology of the majority of species in the garden; (b) the ecological information is just a indicator of the conditions that allow a plant to complete its growing cycle successfully; (c) adoption of these indexes allow a summary representation of the complex situations in the garden and the attributed values to be elaborated. Using a geographical information system (Arc/Info; ESRI, 1998), it was possible to link the geographical database of the garden sectors, with information on the position of each plant, with the alphanumeric database of the Landolt soil moisture indexes supplied by Scotton (1998) referring to each species, and to Pcalculate the water requirement index of the sector (ef) in terms of weighted mean, ef m i1 Ii ni =N, where Ii and ni are the index value and number of individual plants of the ith species in the sector, respectively, and N is the total number of plants in the sector. On the basis of the index value, the sector requirements were classi®ed as very low (ef < 1:5), low (1:5 < ef < 2:5), medium (2:5 < ef < 3:5), high (3:5 < ef < 4:5) and very high (ef > 4:5). The index variability was also studied within the sectors to identify the less homogeneous and most critical situations for the irrigation management. To consider the effect of the cover on evapotranspiration the garden was classi®ed, using information from previous studies (Giardini and Morari, 2000; Morari and Giardini, 2001), simply according to the predominance of the type of habit within the sector: herbaceous or tree. The authors have shown that evapotranspiration in the garden is in¯uenced by type of cover, being higher in the Arboretum where the tall tree crowns increase the transpiration process. Obviously this differentiation is very approximate, but can be justi®ed in this type of practical study. The third classi®cation criterionÐevapotranspiration demandÐwas evaluated as a function of the reduction in global solar radiation caused by the shading of the tall tree crowns on the plants in the central area of the Hortus. In order to identify the more shaded areas, the shade projection produced on the ground by the Arboretum in the different seasons was calculated multiplying the cotangent of the height of the sun above the horizon in the different seasons (Benincasa et al., 1991) by the mean height of the Arboretum, considered as a continuous ``green'' wall (Monteith and Unsworth, 1990). The estimated values, integrated with information from direct observations, allowed two sectors to be distinguished; the ®rst with energy availability limited by the presence of trees during the irrigation season and the second with no such effect. The irrigation class of the sectors (C) was then identi®ed by the overlay of the water requirements thematic map (EF) with the vegetation cover (V) and shading (S) thematic maps. The macro-sectors were then identi®ed grouping the zones with the same irrigation class. 2.5. Irrigation management software The management software allows the allowable soil water depletion to be de®ned, ®xing the soil moisture content upper limit (full point) and lower limit (re®ll point) (Campbell and Campbell, 1982). The software also allows high frequency irrigation cycles to be adopted (<7 days) independently of the lower limit. In fact, with high frequency application the de®nition of allowable soil water depletion becomes relatively less important (Stegman, 1983).
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The software also permits the criteria to be de®ned for elaborating the variability of moisture content measured by the different probes within each macro-sector. A simpli®ed ¯owchart of the control software is reported in Fig. 3. Three criteria were de®ned: (a) average, (b) OR logical and (c) AND logical. (a) Average (weighted): The average of the moisture values measured byPthe probes, weighted according to a weight (li) attributed to each probe so that i li 1, is compared with the upper and lower thresholds de®ned for the corresponding macrosector. The irrigation is terminated when the average moisture content exceeds the full point and started when the average moisture content drops below the re®ll point. (b) OR logical: Upper and lower thresholds are assigned to each probe. The irrigation in an area is terminated when all the probe values exceed the corresponding full points; the irrigation is started when at least one probe value drops below the corresponding refill point. (c) AND logical: Upper and lower thresholds are assigned to each probe as in the previous criterion. In this case however, (i) the irrigation is terminated when at least one probe exceeds the full point and (ii) irrigation is started when all the probes measure values below the corresponding refill point. In general, the full point is identi®ed as the moisture value corresponding to a matric potential of 33 KPa, ®eld capacity. However, there is no sense in talking about ®eld capacity for soils, such as those of the garden, where the water redistribution is in¯uenced by the presence of a shallow water table. In these cases, in fact, hydraulic equilibrium is attained throughout the pro®le above the water table and the matric potential (cm) varies linearly depending on the distance from the water level (z), cm z (Cassel and Nielsen, 1996). To take this situation into account, the software allows the upper threshold to be varied automatically, on the basis of the retention curves and the piezometric head measured by the 3 m depth electronic piezometers. For ease of calculation this threshold is set as equal to a potential value corresponding to the distance between 0.5 of the root zone depth and the water table level. The latter value is determined by calculating the moving average in the 5 days preceding the day of irrigation, with the aim of smoothing any peaks in the water table time series. The option is turned off if the water table rises too close to the surface (<80 cm). 2.6. Irrigation automation set up in the first experimental year In the ®rst year (2000), high-frequency irrigation (Stegman, 1983) was adopted, with intervals close together (from 1 to 4 per day). The software was set up, in consequence, de®ning only the upper threshold and irrigating with a temporal criterion, disregarding the value of the lower threshold. The upper threshold was taken as equal to the water content corresponding to 30 KPa. To consider the parameter's spatial variability, the water retention curve was measured in almost all the TDT positions using both laboratory (Stakman et al., 1969; Klute, 1986) and ®eld methods (Bruce and Luxmoore, 1986), in the latter case in the range varying from 5 to 85 KPa, i.e. that of greatest importance in irrigation management where there is a shallow water table (Table 1). To de®ne the upper limit it was preferred to use the information gathered with the latter methods, as they represent the actual ®eld conditions better than the former.
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Fig. 3. Flowchart of the control software.
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Table 1 Volumetric water content (%) at different matric potential measured in the root layer (0±50 cm) in 11 TDT positions (KPa)
0 2 5 11 30 80 100 1500
Laboratory methods
Field method
Mean
S.D.
Mean
S.D.
52.9 46.1 40.7 38.5 35.3 n.d. 31.0 17.0
3.86 2.98 2.27 2.19 1.98 n.d. 2.29 1.93
n.d. n.d. 37.09 34.36 31.75 25.94 n.d. n.d.
n.d. n.d. 3.19 2.89 3.23 4.62 n.d. n.d.
The water status was monitored by scanning the moisture probes at 2 min intervals and recording the average value every 20 min. The same interval was used to scan the electronic piezometers. At the same time as the moisture content monitoring, the trend of the matric potential of the root layer was determined by placing a series of tensiometers next to each moisture probe at a depth of 20, 30 and, in the Arboretum, 55 cm, and registering the values at 2±3 day intervals. Within the macro-sectors with more than one TDT the moisture values were calculated using the ``average'' criterion, attributing an equal weight to each sensor: the automation system calculated the average of the sensor measurements in the macro-sectors at 2 min intervals, then compared the average soil moisture content to the threshold to determine the need for irrigation. The ``average'' criterion assumes behaviour homogeneity within the macro-sector, a prudent stance adopted with the aim of gaining further knowledge on the physiology of the monitored plants and how representative they are of the individuals not monitored. This aspect is of particular importance for the herbaceous species with high water requirements and fairly shallow root apparatus, which develop water stress before the moisture sensor measures the need to irrigate. Sentinel plants were therefore identi®ed in the macro-sectors with herbaceous cover and, starting in July 2000, the water content in the shallower root layer was monitored every 5±6 days with the TDR method (ESI, 1999b). From a technical point of view, as suggested by Phene and Howell (1984), the performances of the irrigation controller were evaluated considering four basic factors: (a) adequate operation of the system's hardware, such as scanning and measuring instrumentation; (b) the proper algorithms for the system's software; (c) the adequate operation of the input instrumentation, that must respond rapidly to changes in soil water; (d) the adequate operation of the system's output, in terms of go-ahead signals. 3. Results and discussion Fig. 4 shows the distribution of the six macro-sectors in the botanical garden obtained by grouping sectors with the same irrigation class. Of the 20 theoretical classes, produced by
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Fig. 4. Irrigation macro-sectors and real-time measure flags of the TDT and electronic piezometers (S superficial piezometer; S P: deep piezometer). The TDT red flags indicate that irrigation is activated.
193
S:
combining the three identi®ed factors (®ve water requirement classes 2 cover types 2 types of evapotranspiration demand), only seven are in effect represented in the sectors. The maximum technical limit of six macro-sectors that can be controlled made it necessary to group two classes with the same water requirements and evapotranspiration demand, but different cover type, in the same macro-sector. In this case it was assumed that the different evapotranspiration rates would be managed by differing the irrigation volumes within each unit. Furthermore, some fairly small marginal sectors were included in macro-sectors of a different class for technical reasons linked to the connecting electric cables. The types identi®ed (Fig. 4) were the following: (a) low water requirements, with herbaceous cover and limited energy availability (MS1); (b) low water requirements, with herbaceous and partial tree cover and unlimited energy availability (MS2); (c) medium water requirements, with herbaceous cover and limited energy availability (MS3); (d) medium water requirements, with tree and partial herbaceous cover and unlimited energy availability (MS4); and (e) high water requirements with tree cover and unlimited energy availability (MS5). The latter are the sectors with plants of the genus Salix that in the wild usually grow in wet and very wet soils, sometimes in saturated conditions. The alpine plant macro-sector (Alpinum) (MS6), while having a typology similar to MS4, was maintained as an autonomous unit as the irrigation had to be regulated to simulate alpine climatic conditions, with frequent and intense daily rainfall in summer.
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Fig. 5. Box and whiskers of the water requirement index in the different sectors (1: very low; 2: low; 3: medium; 4: high; 5: very high).
The frequency distribution of the water requirement index, represented by the box and whiskers plots in Fig. 5, shows that, with the exception of a few cases, more than one class is represented within each sector. The lowest homogeneity is found in the H. cinctus, where the plants are grouped according to not strictly ecological criteria (collection of plants of systematic interest, rare plant collection etc.). In these sectors 50% of the values can be included in three classes. The situation is less critical outside this area, where in general 50%, if not 100%, of the values are covered by two classes. Obviously this situation
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implies that inevitable compromises must be made in the irrigation management, maintaining a fairly high available water level with the aim of satisfying the plants with higher requirements. The distribution of the TDT probes in the macro-sectors tried to represent the average vegetation cover and its pattern over the growing season. The number was allocated on the basis of the size and heterogeneity of the macro-sector. The number of probes, individual plants included in the monitoring and sentinel plants are presented in Table 2. Fig. 4 shows the positions of the probes and the pairs of electronic piezometers. The monitoring of trees was favoured where these represented the majority of ground cover (MS4) and that of the herbaceous plants in the macro-sectors located within the H. cinctus. The latter were chosen among the species with active cover and therefore transpiring throughout the irrigation season. Unfortunately, just one sensor was placed in both MS5 and MS6. In these macro-sectors, however, the high moisture level maintained in the soil by the frequent irrigations and the modest surface area should reduce the water content spatial variability. 3.1. Irrigation automation performance in the first year Irrigation lasted from the beginning of June to mid-September 2000. Irrigation was regulated in order to maintain a fairly constant level of soil moisture, replacing almost all the evapotranspiration loss. The average available water value never went below 40% for the macro-sectors with low requirements, 50% for those with medium requirements and 70% for those with high water requirements. The highest irrigation volumes, above 450 mm, were supplied in the macro-sectors with highest evapotranspiration rate; in the other areas 200±250 mm were distributed. The situation observed in macro-sector MS4 is presented (Fig. 6) in order to discuss the problems linked to automation. In this area the highest number of probes (the TDT 5 functioned only from the end of July onwards) and the different type of cover give useful information for managing the behaviour variability measured by the probes. The average soil moisture level in the root zone was between 24 and 37%, the maximum limit being reached when rain fell at the beginning of August and beginning of September. The average matric potential never went below 75 KPa and when the rain fell at the end of summer, it rose to 10 KPa. On average, therefore, an optimal water level was guaranteed in the sector (Fig. 6). The monitoring system was ef®cient at terminating the irrigation when rainfall occurred at the end of August and beginning of September, i.e. when the moisture content rose above the ®xed limit of 32%. At the end of August irrigation go-ahead was reset 6 days after the rainfall date. The behaviour at the beginning of September was less clear, as the rainfall events followed one another on different days and the moisture ranged around the full point, alternating days of irrigation go-ahead and days of denial. Some irrigations on these occasions raised the moisture level above the full point and were therefore terminated by the automation system. The sensors responded quickly to a change in the soil moisture level, with a maximum delay, due to time of scanning, of 2 min. Given that the rainfall intensity of the sprinklers is around 0.3 mm min 1, the maximum irrigation inef®ciency that can be expected due to the
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Table 2 Position of the TDT probes: monitored and sentinel plants
MS 1
TDT
Monitored plants
Sentinel plants
3
Rosa arvensis Hudson
15
Thalictrum dioicum L.
Circaea lutetiana L. Aruncus dioicus (Walter) Fernald, Galega officinalis L., Hypericum androsaemum L., Osmunda regalis L., Rorippa austriaca (Crantz) Besser Helianthus multiflorus L., Petasites paradoxus (Retz.) Baumg., Humulus lupulus L., Dipsacus pilosus L., Mentha suaveolens Ehrh., Helianthus giganteus L., Galega officinalis L. Rumex alpinus L. Lysimachia punctata L. Epilobium hirsutum L. Hypericum androsaemum L. Humulus lupulus L. Lysimachia punctata L. Carex pendula Hudson, Molinia coerulea (L.) Moench, Clematis viticella L., Solidago graminifolia (L.) Elliott, Solidago serotina Ait., Scirpus holoschoenus L., Delphinium elatum L., Mentha spicata L.
MS 2
5
Artemisia dracunculus L.
MS 3
13 14 4
Glycyrrhiza chinata L. Geranium pratense L. Symphytum officinale L.
1 2 6 7 8a 11 12a 10 9
Celtis occidentalis L., Picea abies Karsten, Cornus stolonifera Michx Prunus armeniaca L., Acer platanoides L., Acer negundo L. Erythronium dens-canis L., Thuja orientalis L., Lagerstroemia indica L. Thuja orientalis L., Lagerstroemia indica L. Crataegus cuneata Sieb. and Zucc., Cedrus deodora (D. Don) G. Don Crataegus cuneata Sieb. and Zucc., Cedrus deodora (D. Don) G. Don Salix amplexicaulis Bory, Salix fragilis L. Echinum vulgare L., Alchemilla alpina L. Gentiana kochiana Perr. et Song.
MS 4
MS 5 MS 6 a
Probes placed in the 40±70 cm layer.
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Macro-sector
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Fig. 6. Macro-sector MS4: rainfall, irrigation, water table level and average matric potential and soil moisture measured in the root zone.
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Fig. 7. Macro-sector MS4: matric potential and soil moisture measured in the five TDT positions; soil moisture measured in the sentinel plant root zone.
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delay in the termination signal output is 0.6 mm. However, this inef®ciency can be reduced by shortening the scanning time of the sensors. The automatic interruption threshold determined on the basis of the water table level, hypothesising an average root depth of 50 cm, is also reported in Fig. 6 for comparison. It is around 5% higher than the full point adopted and remained practically constant during the summer as the water table level ranged between 140 and 120 cm, with a peak of 80 cm at the beginning of September. The moving average of the water table level consequently ranged between 140 and 100 cm. These variations, assumed to be equal to the variation of the equilibrium potential, are not able to determine appreciable variations in the water content on the basis of the water retention curve measured in the ®eld. It is clear that if the criterion of the automatic interruption threshold had been adopted, the irrigation would not have suffered any interruption during the irrigation season (Fig. 6). The irrigation managed with the ``average'' criterion did not take into account the variability of the values measured by the sensors within the macro-sector. However, this variability can be signi®cant depending on the evapotranspiration of each plant: the differences between the probes were 20% higher on some days (Fig. 7), especially in June, also because the moisture values differed at the start of the season between the area of the H. cinctus (TDT 6) and that outside. These differences implied the coexistence of different potential levels, which anyway, thanks to the frequency of irrigation, never normally went below 0.85 KPa, except in the case of TDT 7 (Fig. 7). To judge from the moisture values registered in the root zone of some sentinel plants (reported in the same ®gure), the distribution of the higher amounts of water would have guaranteed more favourable water conditions for the growth of some (e.g. Scirpus holoschoenus L.). The ``average'' criterion, adopting the same weighting indexes, was therefore unable to take into account these conditions. The same results were observed in the other macro-sectors where the sentinel plants were monitored. 4. Conclusions From the technical and operational point of view the automated system worked properly, allowing integrated management of the information coming from each TDT probe and identifying when to terminate the irrigation. The problems that emerged are due to the dif®culty of taking into account the variability of behaviour of the different plants. Given the site's peculiarities, a prudent approach was taken to the irrigation practice in the ®rst year: the data supplied by the TDT probes were computed according to the ``average'' criterion with the aim of satisfying the prevalent water requirements within the macrosectors. However, this criterion penalised the plants with higher needs. In the light of the ®rst results it seems more appropriate to guarantee optimal water status for the vast majority of species, to irrigate with times and volumes that can maintain a state of high water potential. It is therefore not aimed to reduce variability in the soil moisture content values, due mainly to different evapotranspiration rates, but to favour its shifting above the re®ll point to avoid the appearance of water stress phenomena. Adopting the criterion OR logically appears more suitable for achieving this objective, as it allows the
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minus variant conditions existing in the macro-sectors to be considered and consequently to irrigate. This choice is also favoured by the fact that, at least in the ®rst year, these moisture values have been representative of the water status of the sentinel plants more penalised by the irrigation management. Future studies are necessary to verify the ef®cacy of this criterion in maintaining a water level favourable for plant life, i.e. avoiding any appearance of stress phenomena by either lack of or excess water. Acknowledgements The automated control system was set up with the ®nancial support of CARIPLO and technical assistance from Alessandro Paravicini and Maurizio Massi, TECNO. EL (Formello, Rome). References Abraham, N., Hema, P.S., Saritha, E.K., Subramannian, S., 2000. Irrigation automation based on soil electrical conductivity and leaf temperature. Agric. Water Manag. 45, 145±157. Benincasa, F., Maracchi, G., Rossi, P., 1991. Agrometeorologia. Patron, Bologna. Bruce, R.R., Luxmoore R.J., 1986. Water retention: field methods. In: Klute, A. (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, 1st Edition. ASA, SSSA, Madison WI, pp. 663±686. Campbell, G.S., Campbell, M.D., 1982. Irrigation scheduling using soil moisture measurements: theory and practice. In: Hillel, D. (Ed.), Advances in Irrigation, Vol. 1. Academic Press, New York, pp. 25±42. Cary, J.W., Fisher, H.D., 1983. Irrigation decisions simplified with electronics and soil water sensors. Soil Sci. Soc. Am. J. 47, 1219±1223. Cassel, D.K., Nielsen, D.R., 1996. Field capacity and available water capacity. In: Klute, A. (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, 1st Edition. ASA, SSSA, Madison WI, pp. 901±926. ESI, Environmantal Sensors Inc., 1999a. Gro-Point Soil Moisture Sensor. User Handbook 35 p. (unpublished). ESI, Environmantal Sensors Inc., 1999b. MP-917 Soil Moisture Measurement Instrument. Operational Manual 34 p. (unpublished). ESRI (1998) Arc/Info version 7.2.1. User Manual. Giardini, L., Morari, F., 2000. Ecosistema e Irrigazione dell'Hortus patavinus. Patron, Bologna. Howell, T.A., 1996. Irrigation scheduling research and its impact on water use. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Proceedings of the International Conference, 3±6 November 1996, Evapotranspiration and Irrigation Scheduling, ASAE, San Antonio, TX, pp. 21±33. Klute, A., 1986. Water retention: laboratory methods. In: Klute, A. (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, 1st Edition, ASA, SSSA, Madison, WI, pp. 635±662. È kologische Zeigerwerte zur Schweizer Flora. VeroÈffentlichungen des Geobotanischen Landolt, E., 1977. O Institutes der Eidg. Tech. Hochschule, Stiftung RuÈbel, ZuÈrich. Lukangu, G., Savage, M.J., Johnston, M.A., 1999. Use of sub-hourly soil water content measured with a frequency-domain reflectometer to schedule irrigation of cabbages. Irrig. Sci. 19, 7±13. Monteith, J.L., Unsworth, M.H., 1990. Principles of Environmental Physics, 2nd Edition, Edward Arnold, London. Morari, F., Giardini, L., 2001. Estimating evapotranspiration in the Padova Botanical Garden. Irrig. Sci. 20, 127±137. Phene, C.J., Howell, T.A., 1984. Soil sensor control of high-frequency irrigation systems. Trans. ASAE 81, 392±396. Roberson, M., Fulton, A., Wu, L., Handley, D., Buss, P., Oster, J., 1996. Capacitance probe used for cotton irrigation scheduling. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Proceedings of the International Conference, 3±6 November 1996, Evapotranspiration and Irrigation Scheduling, ASAE, San Antonio, TX, pp. 1109±1114.
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Scotton, M., 1998. Database of the Ecological Indices, personal communication. Stakman, W.P., Valk, G.A., van der Harst, G.G., 1969. Determination of soil moisture retention curves, I. Ist. Land Water Manag. Res. Wageningen (unpublished). Stegman, E.C., 1983. Irrigation scheduling: applied timing criteria. In: Hillel, D. (Ed.), Advances in Irrigation, Vol. 2. Academic Press, New York, pp. 1±30. Topp, G.C., Davis, J.L., Annann, A.P., 1980. Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Res. 16, 574±582. Woodhead, I.M., 1996. A spatial averaging soil moisture sensor for irrigation scheduling. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Proceedings of the International Conference, 3±6 November 1996, Evapotranspiration and Irrigation Scheduling. ASAE, San Antonio, TX, pp. 1115±1121. Zegelin, S.J., White, I., Russell, G.F., 1992. A critique of the TDR technique for determining field soil±water content. In: Topp, G.C., Reynolds, W.D., Green, R.E. (Eds.), Advances in Measurement of Soil Physical Properties: Bringing Theory into Practices, SSSA Special Publication, no. 30. Soil Sci. Soc. Am., Madison, WI, pp. 187±208.
Irrigation automation with heterogeneous vegetation: the case of the Padova botanical garden Francesco Morari*, Luigi Giardini Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Agripolis, UniversitaÁ di Padova, via Romea 16, 35020 Legnaro, Padua, Italy Accepted 10 December 2001
Abstract An automated control system was set up in the Padova Botanical Garden integrating the information on the soil water status supplied by time domain transmissometry (TDT) sensors with the aid of an irrigation microcomputer. The automatic system consisted of four parts: (a) an irrigation network and electronic control unit (microcomputer); (b) a monitoring system of the soil moisture and water table depth; (c) a management software; and (d) a datalogger connecting sensors to the irrigation microcomputer. Sensors were chosen so that they could be remotely connected through cabling over more than 100 m, guaranteeing adequate accuracy and high reliability over time. To take into account the heterogeneity of plant cover, the site was divided into six irrigation macro-sectors managed separately by the automated system. These macro-sectors were selected by classifying the area on the basis of water requirements, cover type and evapotranspiration demand. The software allowed different irrigation criteria to be de®ned, considering the values supplied by the moisture sensors singly or on average. In the ®rst year, the automation worked adequately, allowing the irrigation to be managed on the basis of de®ned thresholds. The irrigation criterion used in the ®rst year for the automated management within macro-sectors, although allowing a favourable water potential to be maintained on average, does not appear to have adequately evaluated the variability of behaviour of the different plants. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Irrigation; Automation; Monitoring system; Geographical information system; Heterogeneous vegetation; Botanical garden
* Corresponding author. Tel.: 39-049-8272857; fax: 39-049-827-2839. E-mail address: [email protected] (F. Morari).
0378-3774/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 1 ) 0 0 1 9 2 - 5
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1. Introduction Different criteria can be used to identify the timing of irrigation (Stegman, 1983). Those based on the soil water status, monitored using electronic sensors and/or simulated by water balance models, currently offer the best application possibilities. These allow the irrigation to be fully automated, using automated data acquisition methods to integrate information from weather stations and the electronic sensors with the irrigation control systems (Howell, 1996). Research in the ®eld of automation (e.g. Cary and Fisher, 1983; Phene and Howell, 1984; Roberson et al., 1996; Abraham et al., 2000) has not only focussed on the criteria for determining irrigation times and volumes, but also on control systems and sensor technology. In the latter ®eld, the range of electronic sensors has been extended, in particular of those that measure the dielectric properties of the soil, such as the time domain re¯ectometry (TDR) sensors, frequency domain re¯ectometry (FDR) sensors, capacitance sensors, etc. There are various problems at this level, from sensor calibration to spatial sampling, from processing and interpreting the signals to verifying the reliability of the instrumentation over time and in ®eld conditions, etc. In the past, research has mainly involved homogeneous cropping conditions, while a case study on irrigation automation in the presence of heterogeneous vegetation, such as in parks and gardens, is lacking. In these heterogeneous areas automation is usually entrusted to electronic control units that activate the opening and closing of the electromagnetic valves at pre-set times and hours. Irrigation is therefore done at ®xed intervals without directly taking the evapotranspiration requirement, rainfall and soil water content into consideration. This creates highly inef®cient situations when, e.g. soil previously saturated by rainfall is irrigated, or when the irrigation volume is no longer able to satisfy increased evapotranspiration demand. The irrigation microcomputer producers try to partly avoid the problem of excess water by connecting the electronic control units to a sensor capable of terminating the irrigation when the rainfall (rain sensor) or soil water content (soil moisture sensor) exceeds a ®xed threshold. These devices can also be inef®cient: in the ®rst case because the rainfall threshold cannot be determined exactly in advance, as it varies depending on the characteristics of the rainfall event (e.g. intensity), previous soil moisture content, evapotranspiration demand, etc.; in the second, because a single sensor cannot represent the soil water status in a non-uniform area like a park or garden. This type of area has a strong spatial variability, arising from variability in soil hydrological properties, nonuniform irrigation, soil surface pro®le and, lastly, from the different absorption pattern by roots and varying evapotranspiration demand. The question of irrigation automation in an area with heterogeneous vegetation was posed at Padova Botanical Garden when setting up a new irrigation system (Giardini and Morari, 2000). The garden (Hortus botanicus patavinus) was founded in 1545, by a decree of the Venetian Republic, to create a place for the display, study of and experimenting with medicinal plants or ``simples''. It is the oldest botanical garden in the world still maintaining its original location, ground-plan and functions. Its historical, cultural and architectural importance is recognised throughout the world and, in 1997, it was included in the UNESCO ``World Heritage'' list of monuments. Following, it was included in the list of ``100 most endangered sites'' compiled by the World Monuments Fund, as intensive
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building operations close to the perimeter of the garden in 1996 had lowered the water table, altering a long-established equilibrium between groundwater and vegetation (Ricceri and Simonini, 1998, personal communication). This prompted the University of Padova, with the collaboration of some private sponsors, to set up an automated irrigation system to prevent any risks of water stress. This paper presents: (a) the automated irrigation system of the Padova Botanical Garden; (b) the technical aspects related to soil water monitoring and, in particular, the spatial sampling issues; (c) the performances of irrigation control and problems encountered in the ®rst experimental year. 2. Materials and methods 2.1. The site The botanical garden extends over a trapezoidal area of 2 ha bounded in the north and west by the Alicorno canal (Fig. 1). Because of its position within the city, the site suffers from the effects that urbanisation, traf®c and industries have on the climate. The climate is sub-humid, with an average annual rainfall of 805 mm. Because of the ``heat island'' effect the average annual temperature is 0.8 8C higher than the rural environment of the Faculty of Agriculture, located 10 km outside of Padova. The buildings that surround the garden act as windbreaks, slowing the speed and altering the main wind directions. Moreover, due to pollution and the shadowing effects of trees and buildings, solar radiation is up to 10% lower than in the rural environment. The FAO-UNESCO classi®cation of the soil is Calcari Urbic Anthrosol (Atuc), silty loam. The low porosity of the soil and its high resistance hinder root growth beneath 60± 70 cm from the soil surface. An impervious clay layer at a depth of 3.50 m gives rise to a water table that ranges in depth between 60 cm during autumn±winter and 160 cm in the summer on average, but with wide ¯uctuations depending on the weather (Giardini and Morari, 2000). Around 2300 species are cultivated in the garden (excluding those in the greenhouses), assembled in collections on the basis of their systematic or ecological characteristics, utilisation or place of origin. In general, the herbaceous collections are in the central area (Hortus cinctus), while the tree ones are around this. The latter include a collection begun in 1700 that has now assumed the characteristics of a small wood (Arboretum) (Fig. 1). 2.2. Automation of the botanical garden irrigation system A plan of the automation system is given in Fig. 2. The system can be divided in four parts: (a) Irrigation network and electronic control unit (microcomputer): The irrigation system consists of about 550 sprinklers controlled by 27 solenoid electromagnetic valves. An electronic control unit that activates the solenoids by means of a 24 V relay manages these valves. The electronic control unit allows the programming of the sequence and
Fig. 1. Map of Botanical Garden of Padova.
Fig. 2. Layout of the automation system.
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times of opening (on) and closing (off) of the valves. It is thus possible to irrigate 27 single sectors with different cycles and volumes. (b) Monitoring system of soil moisture and water table: A series of 15 TDT moisture sensors (Gro-PointTM; ESI, 1999a) and 10 electronic piezometers are scanned and acquired by a datalogger (CR10X, Campbell Scientific) with a 16 differential channel multiplexer (AM416 Campbell Scientific). The 10 piezometers, placed in pairs at two depths (3 and 6 m), are of the open tube type with porous filtering cell; a pressure transducer connected to the datalogger is placed inside the tubes. (c) Management software and workstation: The software, programmed in Visual C, allows different functions to be performed: (1) datalogger functions (definition of the scanning and registration intervals, definition of the automatic reset in the case of central processing unit (CPU) power failure, etc.; (2) instant display on video of the measurements; (3) formulation of the irrigation schedule through the definition of the thresholds and criteria; and (4) downloading, recording and display of the data contained in the datalogger memory. (d) Datalogger connecting unit to the irrigation microcomputer: This represents the core of the automated system. The go-ahead for an irrigation is given by the datalogger that, on the basis of the defined thresholds and criteria, open the connecting circuit between the irrigation microcomputer and the electromagnetic valves through a 12 V relay. The datalogger is equipped with six switches in rotation and is therefore able to automate the irrigation differently in six macro-sectors, composed of one or more sector. The core of the automation software is loaded in the datalogger and not in the CPU, a choice allowing the system to work under adverse environmental conditions, such as sudden changes in tension or prolonged blackouts. The contacts of the relays are normally closed, a position that means the time-dependent automation can work when the datalogger is disconnected. 2.3. The TDT probes The TDT method comes within the wider category of time domain methods. These methods exploit the dielectric behaviour of the soil, which varies as a consequence of changes in water content (Topp et al., 1980). Time domain methods measure the time a fast pulse edge takes to travel along a section of transmission line buried in the soil. Unlike TDR, where the time is measured after the signal has been re¯ectedÐi.e. the signal covers the length of the wave guide twice, in TDT the time is measured at the other end of the transmission line, after the impulse has covered the wave guide only one way (ESI, 1999a). Although the accuracy of the TDR method is better than the TDT method, the latter offers important advantages for irrigation automation. The sensor output (5±50 mA) is simple to analyse and cabling gives remote connections up to 300 m. This aspect is of fundamental importance in the garden where the distance between the datalogger and the sensors is more than 100 m in some cases, and its structural simplicity guarantees robustness and long-life (ESI, 1999a). The level of accuracy measured in controlled laboratory conditions is, for the majority of soils (including that of the garden) 0.03 mm mm 1 (ESI, 1999a). The volume of soil monitored by the probes is 150 mm 230 mm 370 mm.
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2.4. Criteria for positioning the probes The criteria for de®ning the number and position of the probes are a compromise between the need to represent the variability observed in the moisture values in the three dimensions x, y and z and the technical and economic constraints involved in setting up the automated system. In addition, there is the problem of the uniqueness of the place, under the protection of UNESCO, that limits digging operations for positioning the sensors and connecting cables. For these reasons the number of probes was limited to 15, a number that could, after an initial experimental phase, be slightly increased in the future. As a general criterion, when positioning the sensors it was preferred to represent the surface variability (dimensions x, y) as well as possible, rather than the variability observed at depth (dimension z). The probes were therefore placed in an inclined position so as to give integrated measurement of the moisture in the root layer, i.e. between 5±10 and 35± 40 cm. In order to take into account the dynamics of the water below this depth, probes were also placed in the 40±70 cm layer. A vertical or inclined probe can have lower accuracy because of errors linked to the non-linear integration of pro®les with different moisture contents (Woodhead, 1996). However, these errors are in the order of 1% and can therefore be considered acceptable for this application. The advantage is the possibility of obtaining an average depth measurement of soil moisture content with just one sensor. The inclined position was preferred to the vertical one to limit the phenomena of preferential ¯ow (Zegelin et al., 1992). Although it would be possible to automate the irrigation using just one sensor placed horizontally (Campbell and Campbell, 1982), recent studies with FDR sensors have demonstrated the imprecision of the method in determining the exact instant in which to terminate the irrigation (Lukangu et al., 1999). These authors therefore suggest monitoring the moisture throughout the root pro®le. Positioning the probes on the surface is more of a problem as the high species variability must be integrated with a limited number of probes and the current limits in managing the go-ahead for irrigation, which can only be differentiated into six macro-sectors. The ideal automatic irrigation management would, in fact, necessitate macro-sectors (a) composed of individual plants with the same water requirements and cover type (ground cover, height, roughness, etc.) and (b) with the same evapotranspiration demand. Hypothesising adequate hydrological uniformity, these characteristics would allow common intervention thresholds to be de®ned and to have the same soil moisture dynamics. Being purely theoretical, it would be dif®cult to ®nd these conditions in the garden. They anyway represent a criterion for selecting suf®ciently the homogeneous macro-sectors for automated management. De®ning irrigation classes according to the earlier-mentioned parameters (water requirements, cover type and evapotranspiration demand) allowed the sectors to be identi®ed. A parametric method based on the classi®cation proposed by Landolt (1977) was used to classify water requirements. According to this classi®cation the requirements of a species are evaluated subjectively, attributing a merit score from 1 to 5: the value increases as the requirements rise. Landolt's classi®cation refers to the ecological requirements that do not simply express the physiological possibilities of the species. When competition is limited, or absent, the requirement might differ from that indicated by the index. Despite this limitation the system was adopted because: (a) no information is available in the literature
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on the physiology of the majority of species in the garden; (b) the ecological information is just a indicator of the conditions that allow a plant to complete its growing cycle successfully; (c) adoption of these indexes allow a summary representation of the complex situations in the garden and the attributed values to be elaborated. Using a geographical information system (Arc/Info; ESRI, 1998), it was possible to link the geographical database of the garden sectors, with information on the position of each plant, with the alphanumeric database of the Landolt soil moisture indexes supplied by Scotton (1998) referring to each species, and to Pcalculate the water requirement index of the sector (ef) in terms of weighted mean, ef m i1 Ii ni =N, where Ii and ni are the index value and number of individual plants of the ith species in the sector, respectively, and N is the total number of plants in the sector. On the basis of the index value, the sector requirements were classi®ed as very low (ef < 1:5), low (1:5 < ef < 2:5), medium (2:5 < ef < 3:5), high (3:5 < ef < 4:5) and very high (ef > 4:5). The index variability was also studied within the sectors to identify the less homogeneous and most critical situations for the irrigation management. To consider the effect of the cover on evapotranspiration the garden was classi®ed, using information from previous studies (Giardini and Morari, 2000; Morari and Giardini, 2001), simply according to the predominance of the type of habit within the sector: herbaceous or tree. The authors have shown that evapotranspiration in the garden is in¯uenced by type of cover, being higher in the Arboretum where the tall tree crowns increase the transpiration process. Obviously this differentiation is very approximate, but can be justi®ed in this type of practical study. The third classi®cation criterionÐevapotranspiration demandÐwas evaluated as a function of the reduction in global solar radiation caused by the shading of the tall tree crowns on the plants in the central area of the Hortus. In order to identify the more shaded areas, the shade projection produced on the ground by the Arboretum in the different seasons was calculated multiplying the cotangent of the height of the sun above the horizon in the different seasons (Benincasa et al., 1991) by the mean height of the Arboretum, considered as a continuous ``green'' wall (Monteith and Unsworth, 1990). The estimated values, integrated with information from direct observations, allowed two sectors to be distinguished; the ®rst with energy availability limited by the presence of trees during the irrigation season and the second with no such effect. The irrigation class of the sectors (C) was then identi®ed by the overlay of the water requirements thematic map (EF) with the vegetation cover (V) and shading (S) thematic maps. The macro-sectors were then identi®ed grouping the zones with the same irrigation class. 2.5. Irrigation management software The management software allows the allowable soil water depletion to be de®ned, ®xing the soil moisture content upper limit (full point) and lower limit (re®ll point) (Campbell and Campbell, 1982). The software also allows high frequency irrigation cycles to be adopted (<7 days) independently of the lower limit. In fact, with high frequency application the de®nition of allowable soil water depletion becomes relatively less important (Stegman, 1983).
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The software also permits the criteria to be de®ned for elaborating the variability of moisture content measured by the different probes within each macro-sector. A simpli®ed ¯owchart of the control software is reported in Fig. 3. Three criteria were de®ned: (a) average, (b) OR logical and (c) AND logical. (a) Average (weighted): The average of the moisture values measured byPthe probes, weighted according to a weight (li) attributed to each probe so that i li 1, is compared with the upper and lower thresholds de®ned for the corresponding macrosector. The irrigation is terminated when the average moisture content exceeds the full point and started when the average moisture content drops below the re®ll point. (b) OR logical: Upper and lower thresholds are assigned to each probe. The irrigation in an area is terminated when all the probe values exceed the corresponding full points; the irrigation is started when at least one probe value drops below the corresponding refill point. (c) AND logical: Upper and lower thresholds are assigned to each probe as in the previous criterion. In this case however, (i) the irrigation is terminated when at least one probe exceeds the full point and (ii) irrigation is started when all the probes measure values below the corresponding refill point. In general, the full point is identi®ed as the moisture value corresponding to a matric potential of 33 KPa, ®eld capacity. However, there is no sense in talking about ®eld capacity for soils, such as those of the garden, where the water redistribution is in¯uenced by the presence of a shallow water table. In these cases, in fact, hydraulic equilibrium is attained throughout the pro®le above the water table and the matric potential (cm) varies linearly depending on the distance from the water level (z), cm z (Cassel and Nielsen, 1996). To take this situation into account, the software allows the upper threshold to be varied automatically, on the basis of the retention curves and the piezometric head measured by the 3 m depth electronic piezometers. For ease of calculation this threshold is set as equal to a potential value corresponding to the distance between 0.5 of the root zone depth and the water table level. The latter value is determined by calculating the moving average in the 5 days preceding the day of irrigation, with the aim of smoothing any peaks in the water table time series. The option is turned off if the water table rises too close to the surface (<80 cm). 2.6. Irrigation automation set up in the first experimental year In the ®rst year (2000), high-frequency irrigation (Stegman, 1983) was adopted, with intervals close together (from 1 to 4 per day). The software was set up, in consequence, de®ning only the upper threshold and irrigating with a temporal criterion, disregarding the value of the lower threshold. The upper threshold was taken as equal to the water content corresponding to 30 KPa. To consider the parameter's spatial variability, the water retention curve was measured in almost all the TDT positions using both laboratory (Stakman et al., 1969; Klute, 1986) and ®eld methods (Bruce and Luxmoore, 1986), in the latter case in the range varying from 5 to 85 KPa, i.e. that of greatest importance in irrigation management where there is a shallow water table (Table 1). To de®ne the upper limit it was preferred to use the information gathered with the latter methods, as they represent the actual ®eld conditions better than the former.
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Fig. 3. Flowchart of the control software.
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Table 1 Volumetric water content (%) at different matric potential measured in the root layer (0±50 cm) in 11 TDT positions (KPa)
0 2 5 11 30 80 100 1500
Laboratory methods
Field method
Mean
S.D.
Mean
S.D.
52.9 46.1 40.7 38.5 35.3 n.d. 31.0 17.0
3.86 2.98 2.27 2.19 1.98 n.d. 2.29 1.93
n.d. n.d. 37.09 34.36 31.75 25.94 n.d. n.d.
n.d. n.d. 3.19 2.89 3.23 4.62 n.d. n.d.
The water status was monitored by scanning the moisture probes at 2 min intervals and recording the average value every 20 min. The same interval was used to scan the electronic piezometers. At the same time as the moisture content monitoring, the trend of the matric potential of the root layer was determined by placing a series of tensiometers next to each moisture probe at a depth of 20, 30 and, in the Arboretum, 55 cm, and registering the values at 2±3 day intervals. Within the macro-sectors with more than one TDT the moisture values were calculated using the ``average'' criterion, attributing an equal weight to each sensor: the automation system calculated the average of the sensor measurements in the macro-sectors at 2 min intervals, then compared the average soil moisture content to the threshold to determine the need for irrigation. The ``average'' criterion assumes behaviour homogeneity within the macro-sector, a prudent stance adopted with the aim of gaining further knowledge on the physiology of the monitored plants and how representative they are of the individuals not monitored. This aspect is of particular importance for the herbaceous species with high water requirements and fairly shallow root apparatus, which develop water stress before the moisture sensor measures the need to irrigate. Sentinel plants were therefore identi®ed in the macro-sectors with herbaceous cover and, starting in July 2000, the water content in the shallower root layer was monitored every 5±6 days with the TDR method (ESI, 1999b). From a technical point of view, as suggested by Phene and Howell (1984), the performances of the irrigation controller were evaluated considering four basic factors: (a) adequate operation of the system's hardware, such as scanning and measuring instrumentation; (b) the proper algorithms for the system's software; (c) the adequate operation of the input instrumentation, that must respond rapidly to changes in soil water; (d) the adequate operation of the system's output, in terms of go-ahead signals. 3. Results and discussion Fig. 4 shows the distribution of the six macro-sectors in the botanical garden obtained by grouping sectors with the same irrigation class. Of the 20 theoretical classes, produced by
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Fig. 4. Irrigation macro-sectors and real-time measure flags of the TDT and electronic piezometers (S superficial piezometer; S P: deep piezometer). The TDT red flags indicate that irrigation is activated.
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S:
combining the three identi®ed factors (®ve water requirement classes 2 cover types 2 types of evapotranspiration demand), only seven are in effect represented in the sectors. The maximum technical limit of six macro-sectors that can be controlled made it necessary to group two classes with the same water requirements and evapotranspiration demand, but different cover type, in the same macro-sector. In this case it was assumed that the different evapotranspiration rates would be managed by differing the irrigation volumes within each unit. Furthermore, some fairly small marginal sectors were included in macro-sectors of a different class for technical reasons linked to the connecting electric cables. The types identi®ed (Fig. 4) were the following: (a) low water requirements, with herbaceous cover and limited energy availability (MS1); (b) low water requirements, with herbaceous and partial tree cover and unlimited energy availability (MS2); (c) medium water requirements, with herbaceous cover and limited energy availability (MS3); (d) medium water requirements, with tree and partial herbaceous cover and unlimited energy availability (MS4); and (e) high water requirements with tree cover and unlimited energy availability (MS5). The latter are the sectors with plants of the genus Salix that in the wild usually grow in wet and very wet soils, sometimes in saturated conditions. The alpine plant macro-sector (Alpinum) (MS6), while having a typology similar to MS4, was maintained as an autonomous unit as the irrigation had to be regulated to simulate alpine climatic conditions, with frequent and intense daily rainfall in summer.
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Fig. 5. Box and whiskers of the water requirement index in the different sectors (1: very low; 2: low; 3: medium; 4: high; 5: very high).
The frequency distribution of the water requirement index, represented by the box and whiskers plots in Fig. 5, shows that, with the exception of a few cases, more than one class is represented within each sector. The lowest homogeneity is found in the H. cinctus, where the plants are grouped according to not strictly ecological criteria (collection of plants of systematic interest, rare plant collection etc.). In these sectors 50% of the values can be included in three classes. The situation is less critical outside this area, where in general 50%, if not 100%, of the values are covered by two classes. Obviously this situation
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implies that inevitable compromises must be made in the irrigation management, maintaining a fairly high available water level with the aim of satisfying the plants with higher requirements. The distribution of the TDT probes in the macro-sectors tried to represent the average vegetation cover and its pattern over the growing season. The number was allocated on the basis of the size and heterogeneity of the macro-sector. The number of probes, individual plants included in the monitoring and sentinel plants are presented in Table 2. Fig. 4 shows the positions of the probes and the pairs of electronic piezometers. The monitoring of trees was favoured where these represented the majority of ground cover (MS4) and that of the herbaceous plants in the macro-sectors located within the H. cinctus. The latter were chosen among the species with active cover and therefore transpiring throughout the irrigation season. Unfortunately, just one sensor was placed in both MS5 and MS6. In these macro-sectors, however, the high moisture level maintained in the soil by the frequent irrigations and the modest surface area should reduce the water content spatial variability. 3.1. Irrigation automation performance in the first year Irrigation lasted from the beginning of June to mid-September 2000. Irrigation was regulated in order to maintain a fairly constant level of soil moisture, replacing almost all the evapotranspiration loss. The average available water value never went below 40% for the macro-sectors with low requirements, 50% for those with medium requirements and 70% for those with high water requirements. The highest irrigation volumes, above 450 mm, were supplied in the macro-sectors with highest evapotranspiration rate; in the other areas 200±250 mm were distributed. The situation observed in macro-sector MS4 is presented (Fig. 6) in order to discuss the problems linked to automation. In this area the highest number of probes (the TDT 5 functioned only from the end of July onwards) and the different type of cover give useful information for managing the behaviour variability measured by the probes. The average soil moisture level in the root zone was between 24 and 37%, the maximum limit being reached when rain fell at the beginning of August and beginning of September. The average matric potential never went below 75 KPa and when the rain fell at the end of summer, it rose to 10 KPa. On average, therefore, an optimal water level was guaranteed in the sector (Fig. 6). The monitoring system was ef®cient at terminating the irrigation when rainfall occurred at the end of August and beginning of September, i.e. when the moisture content rose above the ®xed limit of 32%. At the end of August irrigation go-ahead was reset 6 days after the rainfall date. The behaviour at the beginning of September was less clear, as the rainfall events followed one another on different days and the moisture ranged around the full point, alternating days of irrigation go-ahead and days of denial. Some irrigations on these occasions raised the moisture level above the full point and were therefore terminated by the automation system. The sensors responded quickly to a change in the soil moisture level, with a maximum delay, due to time of scanning, of 2 min. Given that the rainfall intensity of the sprinklers is around 0.3 mm min 1, the maximum irrigation inef®ciency that can be expected due to the
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Table 2 Position of the TDT probes: monitored and sentinel plants
MS 1
TDT
Monitored plants
Sentinel plants
3
Rosa arvensis Hudson
15
Thalictrum dioicum L.
Circaea lutetiana L. Aruncus dioicus (Walter) Fernald, Galega officinalis L., Hypericum androsaemum L., Osmunda regalis L., Rorippa austriaca (Crantz) Besser Helianthus multiflorus L., Petasites paradoxus (Retz.) Baumg., Humulus lupulus L., Dipsacus pilosus L., Mentha suaveolens Ehrh., Helianthus giganteus L., Galega officinalis L. Rumex alpinus L. Lysimachia punctata L. Epilobium hirsutum L. Hypericum androsaemum L. Humulus lupulus L. Lysimachia punctata L. Carex pendula Hudson, Molinia coerulea (L.) Moench, Clematis viticella L., Solidago graminifolia (L.) Elliott, Solidago serotina Ait., Scirpus holoschoenus L., Delphinium elatum L., Mentha spicata L.
MS 2
5
Artemisia dracunculus L.
MS 3
13 14 4
Glycyrrhiza chinata L. Geranium pratense L. Symphytum officinale L.
1 2 6 7 8a 11 12a 10 9
Celtis occidentalis L., Picea abies Karsten, Cornus stolonifera Michx Prunus armeniaca L., Acer platanoides L., Acer negundo L. Erythronium dens-canis L., Thuja orientalis L., Lagerstroemia indica L. Thuja orientalis L., Lagerstroemia indica L. Crataegus cuneata Sieb. and Zucc., Cedrus deodora (D. Don) G. Don Crataegus cuneata Sieb. and Zucc., Cedrus deodora (D. Don) G. Don Salix amplexicaulis Bory, Salix fragilis L. Echinum vulgare L., Alchemilla alpina L. Gentiana kochiana Perr. et Song.
MS 4
MS 5 MS 6 a
Probes placed in the 40±70 cm layer.
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Macro-sector
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Fig. 6. Macro-sector MS4: rainfall, irrigation, water table level and average matric potential and soil moisture measured in the root zone.
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Fig. 7. Macro-sector MS4: matric potential and soil moisture measured in the five TDT positions; soil moisture measured in the sentinel plant root zone.
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delay in the termination signal output is 0.6 mm. However, this inef®ciency can be reduced by shortening the scanning time of the sensors. The automatic interruption threshold determined on the basis of the water table level, hypothesising an average root depth of 50 cm, is also reported in Fig. 6 for comparison. It is around 5% higher than the full point adopted and remained practically constant during the summer as the water table level ranged between 140 and 120 cm, with a peak of 80 cm at the beginning of September. The moving average of the water table level consequently ranged between 140 and 100 cm. These variations, assumed to be equal to the variation of the equilibrium potential, are not able to determine appreciable variations in the water content on the basis of the water retention curve measured in the ®eld. It is clear that if the criterion of the automatic interruption threshold had been adopted, the irrigation would not have suffered any interruption during the irrigation season (Fig. 6). The irrigation managed with the ``average'' criterion did not take into account the variability of the values measured by the sensors within the macro-sector. However, this variability can be signi®cant depending on the evapotranspiration of each plant: the differences between the probes were 20% higher on some days (Fig. 7), especially in June, also because the moisture values differed at the start of the season between the area of the H. cinctus (TDT 6) and that outside. These differences implied the coexistence of different potential levels, which anyway, thanks to the frequency of irrigation, never normally went below 0.85 KPa, except in the case of TDT 7 (Fig. 7). To judge from the moisture values registered in the root zone of some sentinel plants (reported in the same ®gure), the distribution of the higher amounts of water would have guaranteed more favourable water conditions for the growth of some (e.g. Scirpus holoschoenus L.). The ``average'' criterion, adopting the same weighting indexes, was therefore unable to take into account these conditions. The same results were observed in the other macro-sectors where the sentinel plants were monitored. 4. Conclusions From the technical and operational point of view the automated system worked properly, allowing integrated management of the information coming from each TDT probe and identifying when to terminate the irrigation. The problems that emerged are due to the dif®culty of taking into account the variability of behaviour of the different plants. Given the site's peculiarities, a prudent approach was taken to the irrigation practice in the ®rst year: the data supplied by the TDT probes were computed according to the ``average'' criterion with the aim of satisfying the prevalent water requirements within the macrosectors. However, this criterion penalised the plants with higher needs. In the light of the ®rst results it seems more appropriate to guarantee optimal water status for the vast majority of species, to irrigate with times and volumes that can maintain a state of high water potential. It is therefore not aimed to reduce variability in the soil moisture content values, due mainly to different evapotranspiration rates, but to favour its shifting above the re®ll point to avoid the appearance of water stress phenomena. Adopting the criterion OR logically appears more suitable for achieving this objective, as it allows the
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minus variant conditions existing in the macro-sectors to be considered and consequently to irrigate. This choice is also favoured by the fact that, at least in the ®rst year, these moisture values have been representative of the water status of the sentinel plants more penalised by the irrigation management. Future studies are necessary to verify the ef®cacy of this criterion in maintaining a water level favourable for plant life, i.e. avoiding any appearance of stress phenomena by either lack of or excess water. Acknowledgements The automated control system was set up with the ®nancial support of CARIPLO and technical assistance from Alessandro Paravicini and Maurizio Massi, TECNO. EL (Formello, Rome). References Abraham, N., Hema, P.S., Saritha, E.K., Subramannian, S., 2000. Irrigation automation based on soil electrical conductivity and leaf temperature. Agric. Water Manag. 45, 145±157. Benincasa, F., Maracchi, G., Rossi, P., 1991. Agrometeorologia. Patron, Bologna. Bruce, R.R., Luxmoore R.J., 1986. Water retention: field methods. In: Klute, A. (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, 1st Edition. ASA, SSSA, Madison WI, pp. 663±686. Campbell, G.S., Campbell, M.D., 1982. Irrigation scheduling using soil moisture measurements: theory and practice. In: Hillel, D. (Ed.), Advances in Irrigation, Vol. 1. Academic Press, New York, pp. 25±42. Cary, J.W., Fisher, H.D., 1983. Irrigation decisions simplified with electronics and soil water sensors. Soil Sci. Soc. Am. J. 47, 1219±1223. Cassel, D.K., Nielsen, D.R., 1996. Field capacity and available water capacity. In: Klute, A. (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, 1st Edition. ASA, SSSA, Madison WI, pp. 901±926. ESI, Environmantal Sensors Inc., 1999a. Gro-Point Soil Moisture Sensor. User Handbook 35 p. (unpublished). ESI, Environmantal Sensors Inc., 1999b. MP-917 Soil Moisture Measurement Instrument. Operational Manual 34 p. (unpublished). ESRI (1998) Arc/Info version 7.2.1. User Manual. Giardini, L., Morari, F., 2000. Ecosistema e Irrigazione dell'Hortus patavinus. Patron, Bologna. Howell, T.A., 1996. Irrigation scheduling research and its impact on water use. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Proceedings of the International Conference, 3±6 November 1996, Evapotranspiration and Irrigation Scheduling, ASAE, San Antonio, TX, pp. 21±33. Klute, A., 1986. Water retention: laboratory methods. In: Klute, A. (Ed.), Methods of Soil Analysis. Physical and Mineralogical Methods, 1st Edition, ASA, SSSA, Madison, WI, pp. 635±662. È kologische Zeigerwerte zur Schweizer Flora. VeroÈffentlichungen des Geobotanischen Landolt, E., 1977. O Institutes der Eidg. Tech. Hochschule, Stiftung RuÈbel, ZuÈrich. Lukangu, G., Savage, M.J., Johnston, M.A., 1999. Use of sub-hourly soil water content measured with a frequency-domain reflectometer to schedule irrigation of cabbages. Irrig. Sci. 19, 7±13. Monteith, J.L., Unsworth, M.H., 1990. Principles of Environmental Physics, 2nd Edition, Edward Arnold, London. Morari, F., Giardini, L., 2001. Estimating evapotranspiration in the Padova Botanical Garden. Irrig. Sci. 20, 127±137. Phene, C.J., Howell, T.A., 1984. Soil sensor control of high-frequency irrigation systems. Trans. ASAE 81, 392±396. Roberson, M., Fulton, A., Wu, L., Handley, D., Buss, P., Oster, J., 1996. Capacitance probe used for cotton irrigation scheduling. In: Camp, C.R., Sadler, E.J., Yoder, R.E. (Eds.), Proceedings of the International Conference, 3±6 November 1996, Evapotranspiration and Irrigation Scheduling, ASAE, San Antonio, TX, pp. 1109±1114.
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