Agricultural Water Management 159 (2015) 123–138
Contents lists available at ScienceDirect
Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat
Dynamic prescription maps for site-specific variable rate irrigation of cotton夽 Susan A. O’Shaughnessy ∗ , Steven R. Evett, Paul D. Colaizzi USDA-ARS, Conservation and Production Research Laboratory, P.O. Drawer 10, Bushland, TX 79012, USA
a r t i c l e
i n f o
Article history: Received 4 September 2014 Received in revised form 3 May 2015 Accepted 7 June 2015 Keywords: Center pivot Infrared thermometry Irrigation scheduling Plant feedback Wireless sensor network
a b s t r a c t A prescription map is a set of instructions that controls a variable rate irrigation (VRI) system. These maps, which may be based on prior yield, soil texture, topography, or soil electrical conductivity data, are often manually applied at the beginning of an irrigation season and remain static. The problem with static prescription maps is that they ignore spatiotemporal changes in crop water status. In a two-year study (2012 and 2013), a plant feedback system, including a wireless sensor network of infrared thermometers (IRTs), was used to develop dynamic prescription maps to accomplish adaptive irrigation scheduling for cotton (Gossypium hirsutum L.). One-half of a center pivot field was divided into manually and plant feedbackcontrolled irrigation treatment plots. Irrigation treatments were at three levels, 75, 50 and 25 percent of full as defined by either replenishment of crop water use to field capacity or by the equivalent threshold of the IRT sensed crop water stress. The system accepted user input to control irrigation for the manual treatment plots (I75M , I50M , and I25M ), and calculated and compared a thermal stress index for each plant feedback-controlled treatment plot (I75C , I50C and I25C ) with a pre-determined threshold for automated irrigation scheduling. The effectiveness of the plant feedback irrigation scheduling system was evaluated by comparing measured lint yield, crop water use (ETc ), and water use efficiency (WUE) with the manually scheduled treatment plots. Results for both years indicated that average lint yields were similar between the manual and plant feedback-control plots at the I75 level (181 and 182 g m−2 , respectively, in 2012; 115 and 103 g m−2 , respectively, in 2013) and I50 level (146 and 164 g m−2 , respectively, in 2012; 95 and 117 g m−2 , respectively, in 2013). At the I25 level, average lint yield was significantly greater for the plant feedback-compared with the manual-control treatment plots (142 g m−2 and 92 g m−2 , respectively), but the mean amount of irrigation was twice that of the manual-control plots. Mean water use efficiencies (WUE) within the same irrigation treatment levels were similar between methods. Importantly, the automatic plant feedback system did not require the time consuming and expensive manual reading of neutron probe access tubes that was required to schedule the manual treatments. These results demonstrate that the integration of a plant feedback system with a commercial VRI system could be used to control site-specific irrigation management for cotton at higher irrigation treatment levels, i.e., I75 percent and I50 percent of full. Such a system can facilitate the use of a VRI system by automating prescription map coding and providing dynamic irrigation control instructions to meet variable crop water needs throughout the irrigation season. As of yet, further research is required to maintain automatic deficit irrigation at a level equivalent to 25 percent replenishment of crop water use relative to field capacity. Published by Elsevier B.V.
夽 The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual’s income is derived from any public assistance program. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer. ∗ Corresponding author. Fax: +806 356 5750. E-mail address:
[email protected] (S.A. O’Shaughnessy). http://dx.doi.org/10.1016/j.agwat.2015.06.001 0378-3774/Published by Elsevier B.V.
124
S.A. O’Shaughnessy et al. / Agricultural Water Management 159 (2015) 123–138
1. Introduction Variable rate irrigation (VRI) systems provide the flexibility of delivering accurate but variable depths of water along a sprinkler lateral or in the direction of sprinkler travel (Dukes and Perry, 2006; Han et al., 2009; Chávez et al., 2010; Evans et al., 2010; O’Shaughnessy et al., 2013a). These systems are now commercially available and could be used as a tool for improving crop water management by delivering water to plants where needed, when the crop demands it and in the appropriate amounts. Prescription maps, sets of coded instructions, control pivot movement and flows to apply specific irrigation depths for each management zone. Management zones are defined by the radial extent of controlled sets of drop hoses along the sprinkler lateral, and by the angular extent of pivot rotation in the direction of travel. Prescription maps for a VRI system are usually generated at the beginning of a growing season, manually uploaded to the irrigation control panel (Evans et al., 2013), and typically based on geo-referenced data from prior yields, soil texture, apparent soil electrical conductivity (ECa ), or topographical elevations. Static prescription maps are problematic in the sense that spatiotemporal variability of crop water stress throughout the growing season is overlooked. Such variability can be a consequence of spatially variable precipitation, topography, variable soil infiltration rates and soil storage capacity (Sadler et al., 2005), variable plant stand, or disease and pest infestation (Falkenberg et al., 2007). Additionally crop evapotranspiration varies over a growing season and between years, due to fluxes in radiation, temperature, wind speed, and vapor pressure deficit. Dynamic prescription maps resulting from continuous monitoring and assessment of climatological and crop data are highly desirable. Crop water stress has been characterized using infrared thermometry (Nielsen and Anderson, 1989), and theoretical (Jackson et al., 1981) and empirical (Idso and Reginato, 1982; Wanjura and Upchurch, 2000; Barbosa da Silva and Rao, 2005) thermal stress indices. Information from IRTs mounted on a moving sprinkler irrigation system with a global positioning system (GPS) has been demonstrated to provide spatial and temporal crop water status (Sadler et al., 2002; Peters and Evett, 2007; O’Shaughnessy et al., 2011a) at the field scale. By continuous crop canopy temperature and meteorological monitoring, the use of IRTs on a moving VRI irrigation sprinkler provides plant feedback control (Fig. 1) for irrigation scheduling. Control of irrigation is possible using threshold stress indices (Nielsen and Gardner, 1987; Garrot et al., 1994; Evett et al., 1996, 2002; Barbosa da Silva and Rao, 2005; Gontia and Tiwari, 2008). The amount of irrigation to be delivered can be determined using multiples of peak daily water use (i.e., frequency of repeat travel over a management zone multiplied by daily peak crop water use) (Peters and Evett, 2008; O’Shaughnessy and Evett, 2010) or by estimating crop water requirements using weather based measurements and crop coefficients. After the sprinkler travels over the field, the need to schedule an irrigation is automatically re-assessed for each management zone. In the strictest sense, plant feedback systems are open-loop feedback systems. However, crop response (yields and water use efficiencies) using plant feedback systems for irrigation scheduling has been similar to or better than crop response from irrigation scheduling using direct soil water measurements with the neutron probe (BIOTIC method, Upchurch et al., 1996; the time-temperature-threshold method, Evett et al., 1996; Peters and Evett, 2008; O’Shaughnessy and Evett, 2010; CWSI and time threshold, O’Shaughnessy et al., 2012; and the integrated CWSI (i CWSI), O’Shaughnessy et al., 2013b). If prescription maps are constructed using geo-referenced plant sensing data and pre-established thresholds to indicate where, when, and the depth of irrigation required, then dynamic sitespecific instructions for irrigation scheduling can be developed
throughout the irrigation season. These dynamic prescription maps could direct site-specific irrigation according to crop water needs, potentially improving crop water use efficiency by irrigating the crop only when it needs it, and reducing the likelihood of excessive irrigations, runoff and deep percolation. Using a computer for centralized data collection, storage and control, construction and uploading of the maps can be automated. However, to date, the effectiveness of dynamic prescription maps for controlling sitespecific variable rate irrigation using plant feedback methods has not been demonstrated. Therefore, our specific objectives were to: (1) assess the feasibility of developing dynamic prescription maps from a plant feedback system for irrigation control of a center pivot outfitted with a commercial VRI package; and (2) evaluate irrigation control via the dynamic prescription maps by comparing cotton responses of lint yield, water use (ETc ), and crop water use efficiency (WUE) from plots irrigated automatically using the plant feedback system with the responses of plots irrigated manually based on weekly neutron probe readings. 2. Materials and methods 2.1. Experimental site and irrigation system The two-year study was conducted at the Conservation and Production Research Laboratory (CPRL), Bushland, Texas (35◦ 11 N, 102◦ 06 W, 1170 m above mean sea level). The field soil was a Pullman clay loam, a fine, mixed, superactive, thermic, Torrertic Paleustoll (Soil Survey Staff, 2004). The field capacity (0.33 m3 m−3 ) and wilting point (0.18 m3 m−3 ) (Unger and Pringle, 1981) water contents were assumed uniform across the center pivot field. The climate is semi-arid with an average annual rainfall of 470 mm. Upland cotton, delta pine, variety 1212RFBG21 was planted on May 19, 2012, day of year (DOY) 140, and on May 31, 2013 (DOY 151) at a rate of 198,000 seeds ha−1 . The planting date was delayed in 2013 due to low spring soil temperatures. Irrigations were applied in alternate furrows with low energy precision application (LEPA) drag socks (Lyle and Bordovsky, 1983) in every other furrow. Furrows were diked to minimize runoff (Schneider and Howell, 1995) and to provide temporary detention required for LEPA irrigation. 2.2. System design The plant feedback system (Fig. 1) was comprised of a wireless sensor network (WSN) system of infrared thermometers (IRTs) (Melexis, MLX90614, Ieper, Belgium) and meteorological data integrated with a commercial VRI system. The embedded computer (base station) was located at the pivot point and provided central data collection and data processing. Data collected at the base-station was time stamped and geo-spatially referenced. Prescription maps were built every two days using data manually entered by the user and information calculated by the embedded computer. The manual input was the amount of water to be applied to replenish 100% of mean soil water depletion to field capacity to the manual treatment plots in the I75 treatment level. Irrigation applied was 75%, 50%, and 25% of the amount entered by the user, and was calculated within the embedded computer. The integrated crop water stress index (i CWSI) values were calculated for daylight hours from continuous crop canopy temperature and meteorologi-
1 The use of trade, firm, or corporation names in this article is for the information and convenience of the reader. Such use does not constitute an official endorsement or approval by the United States Department of Agriculture or the Agricultural Research Service of any product or service to the exclusion of others that may be suitable.
S.A. O’Shaughnessy et al. / Agricultural Water Management 159 (2015) 123–138
125
Fig. 1. Design of integrated system of wireless sensor network and variable rate irrigation sprinkler controlled with dynamic plant feedback prescription maps.
cal data collected over a 24 h period. More details are provided later in this section. 2.3. Variable rate irrigation system The irrigation system was a three-span center pivot with a 131 m lateral, retrofitted with a commercial variable rate irrigation (VRI) package (Valmont Industries1 , Valley, Nebr.). Each drop hose was plumbed with a hydraulic valve, which pulsed water “on” and “off” and regulated to 41.4 kPa. The pivot lateral was configured for 12 irrigation zones. Each irrigation zone was comprised of six sprinkler drop hoses that were hydraulically connected and actuated by a single electronic solenoid valve. A programmable logic controller in the irrigation system’s control panel regulated the VRI hardware using the prescription map uploaded into the control panel using an RS-232 serial connection between the computer and control panel. A wide area augmentation system (WAAS) corrected GPS, with a differential position accuracy of ±3 m, communicated with the programmable logic controller using power line carrier communication. 2.4. Sensor network systems The wireless sensor network (WSN) was comprised of a base station (embedded computer) at the pivot point, infrared thermometers (IRTs) installed on the pivot lateral and in the field below as described by O’Shaughnessy et al. (2013b), and a weather station
located in near proximity (8 m from the pivot point). The wireless IRTS (simple IRTs) and multiband radiometers (MBRs) were developed at the USDA-ARS CPRL. The thermal detectors of each instrument were calibrated in a temperature controlled chamber against a commercial black body prior to the beginning of each growing season using methods similar to those described in O’Shaughnessy et al. (2011b), in ambient temperatures of 15 ◦ C, 25 ◦ C, 35 ◦ C, and 45 ◦ C, while the black body temperature was stepped down from 55 ◦ C to 10 ◦ C in increments of 5 ◦ C. The infrared thermal detector is an off-the-shelf sensor (Melexis, Model MLX90614BCF, Belgium) with a field of view (FOV) of 28◦ and a pass filter ranging from 8 to 14 m. The temperature accuracy is 0.5 ◦ C over the range of 0–50 ◦ C for the target and sensor body temperature. The measurement resolution is 0.01 ◦ C. In 2012, eight simple IRTs were mounted on masts located on the pivot lateral at the border of each concentric plot looking inwards at an oblique angle (angle of 135◦ relative to the vertical mast on which the sensor was mounted). The sensor was pointed slightly forward at an azimuth angle of 70◦ between the direction of pivot travel and the crop row. The simple IRTs were housed in weatherproof cases milled from solid white ploy-vinyl chloride (PVC), and measured 17.8 cm in length and 4.4 cm in diameter. The sensors located on the pivot lateral viewed the cropped field forward of the irrigations and were maintained at a height approximately 1.0 m above the crop canopy. Additionally, three IRTs were placed in the well-watered inner border region (Fig. 2) at nadir-looking views and adjusted to maintain a height of 1.0 m above the crop canopy.
126
S.A. O’Shaughnessy et al. / Agricultural Water Management 159 (2015) 123–138
Fig. 2. Experimental field layout; plots designated with the letter “M” indicate manual irrigation scheduling using weekly readings from a neutron probe, and those with a “C” indicate irrigation scheduling using the plant feedback algorithm (i CWSI) for (a) 2012 and (b) 2013.
Data from these sensors were used as reference temperature measurements for the scaling algorithm (described below). In 2013, wireless multiband radiometers were used in lieu of simple IRTs. The sensor housing measured 23.0 cm in length and was 6.5 cm in diameter. The MBRs contained three filter/detectors with bands in the visible range, red (685 ± 35 nm), blue (450 ± 40 nm), and green (560 ± 10 nm) range; one filter/detector with a band in the near infrared (NIR) (880 ± 50 nm) range; and the infrared temperature sensor (Melexis). The white PVC housing for the photodiode detectors was designed to collimate light onto each detector/filter (Intor, model T-5, Soccoro, NM). The MBRs were developed by ARS scientists at the CPRL in Bushland, TX. The MBR footprint was a circular pattern with a FOV determined to be 28◦ . These sensors were mounted as described for the simple IRTs used in 2012. Reflectance measurements from visible bands within the MBR were individually calibrated against red, blue, and green (5 cm × 5 cm) samples in a color chart (Edmund Optics, Barrington,
NJ) in full sunlight and then cross-calibrated against a reference sensor over larger colored targets. During the individual calibrations, each MBR was positioned in a nadir view over the color sample of interest. The readings were taken in full sunlight to enable us to determine the maximum response of each band, while the minimum response was determined by capping the sensors with an opaque cover. This procedure provided a qualitative means to determine if the photodiode detectors were responding appropriately. The cross-calibration procedure required simultaneous reflectance measurements of multiple sensors over large (2 m × 2 m) color targets (red, blue and green). These targets were made of plywood and spray-painted to achieve an even coat of color. One of the eight MBRs was chosen as a reference sensor and was always included during batch measurements with the other MBRs. During the batch measurements, the MBRs were placed in a nadir-looking view over the large target, and measurements were made over daylight hours. A band-to-band cross-calibration was made between each photodiode detector of a MBR and the corre-
S.A. O’Shaughnessy et al. / Agricultural Water Management 159 (2015) 123–138
sponding photodiode detector of the reference MBR. Coefficients from the resulting linear regression equations were used to establish custom calibration equations for each photodiode detector of each MBR. Coefficients were entered into the base station. Even with these calibrations, it was expected that reflectance readings from the MBRs would vary due to differences at the circuit board level, height above the canopy, and variations in the viewing angle from manually adjusting and aiming the MBRs. Prior to deploying the sensors on the pivot lateral, continuous reflectance measurements were then made over dry and wet soil (Pullman clay loam) and green vegetation-grass fetch at the CPRL weather pen and winter wheat planted on the laboratory. The grass represented an example of a pixel that was comprised nearly of 100% vegetation, the winter wheat samples represented pixels that were of mixed percent vegetation and soil. A customized metal stand was built to accommodate six MBRs. An algorithm was used to qualify the thermal data as being relevant to either plants or some mixture of soil and plants in the viewing area. The algorithm was based on a normalized difference vegetative index (NDVI) (Rouse et al., 1974) calculated with reflectance measurements taken from the NIR and red bands (NIR − red )/(NIR + red ) and concomitant with the reading of the thermal band. Thermal measurements recorded in conjunction with NDVI values less than 0.25 were not used to calculate the i CWSI. Radiometric instruments capturing mixed pixels of vegetation and soil are a common problem in agricultural applications. Jiang et al. (2006) demonstrated that NDVI over partially vegetated surfaces were non-linear and that NDVI calculated at different resolutions were not comparable. Additionally, the detection of water stress and disease using thermal images can be confounded with pixels containing a mixture of soil and vegetation (Jones and Sirault, 2014). When canopy temperature is used for irrigation scheduling, temperatures resulting from pixels comprised mainly of soil can result in false positive triggers and over-irrigation in the long run. After the analyses of NDVI from multiple measurements over grass, wheat and soil, we determined that at NDVI values
