A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity
Abstract
:1. Introduction
2. Materials and Methods
2.1. Root Zone Water Quality Model (RZWQM2)
2.2. Decision Support System for Irrigation Scheduling (DSSIS) Framework
2.2.1. The DSSIS Framework Design
- Irrigation pipeline system, consisting of polyvinyl chloride (PVC) pipe, drip irrigation pipe, and valves. The drip irrigation pipes are equipped with pressure compensation type emitters with a flow rate of 5 Lh−1 at a pressure of 1–2 bar. The distance between two emitters is 0.1 m to match the plant spacing.
- Irrigation control software, consisting of weather data acquisition (online for the future and site-specific for current), RZWQM2 model, and IrrSch decision and control software. The development of this IrrSch software can be found in Gu et al. [40]. The system functions as follows:
- ▪
- The RZWQM2 model, with crop and soil parameters calibrated using a historical field experiment from 2007–2014 [41], is installed on a PC;
- ▪
- The IrrSch software retrieves current day weather information from an on-site weather station as well as 4-day weather forecasts from a weather Application Program Interface (API) (http://api.openweathermap.org), and subsequently feeds into RZWQM2;
- ▪
- RZWQM2 is called by IrrSch and executed to predict the water stress factor, crop ET, and θ for the current and four upcoming days;
- ▪
- When the current day’s predicted water stress factor is less than a user defined threshold, an irrigation event is triggered and the amount of water to be supplied is calculated using the θfc, the predicted current θ, the predicted crop rooting depth, and the total amount of current and forecast 4-day rainfall.
- Irrigation control hardware, consisting of soil moisture sensors, electromagnetic valves, the field programmable logic controller (F-PLC), the site of programmable logic controller (S-PLC), frequency conversion controller (FCC), and the user operating the S-PLC to facilitate irrigation;
- Peripheral equipment, consisting of a reservoir, circulating pump, strainer, and groundwater pumping station, to secure a water supply for crops.
2.2.2. The Network Information Transfer in the DSSIS
2.3. Irrigation Control Software
2.3.1. Information Acquisition
2.3.2. Irrigation Schedule Software
- The first interface (Figure 3) hosts seven steps through which users can input basic information into the software. Steps 1 through 6 include entering previously calibrated and validated information regarding RZWQM2 model parameters [41]. Because some parameters (planting density, tillage, etc.) do not need to be updated annually, users simply update the planting date. The seventh step includes three subroutines, but the user only controls two of these:
- ▪
- the “Update Weather Data” button to read the weather data files which have been downloaded from the weather station;
- ▪
- the “Calculation” button to run the RZWQM2 model and for the IrrSch to enter the second interface.
- The second interface is the irrigation operations interface (Figure 4), which serves to view the information regarding water stress and crop growth, and provides users with the ability to send an irrigation instruction to the SCM. On this interface, the “Calculate” button may be used to update the value of soil water stress factor (SWFAC, 0 = extreme stress and 1 = no stress), root depth and irrigation amount and timing, as well as rainfall and crop biomass. If the value of water stress factor is less than 0.9, users must input an irrigation amount and time by pressing the “+” button. In other words, irrigation will not be triggered if SWFAC > 0.9. The suggested maximum single irrigation amount is presented in the tables of the second interface. The “Send to SCM” button will send irrigation instructions to the S-PLC and activate the FCC controlled circulating pump. The whole irrigation management operation may be automated to promote the adoption and commercialization of this newly developed irrigation management system. Root length is determined within RZWQM by simulated root growth in each soil layer.
2.3.3. The Water Stress Factor Algorithm in DSSIS
- —volumetric heat capacity of air (MJ m−3 °C−1),
- k1, k2 and k3—dimensionless constants drawn from DSSAT, v3.5: k1 = 1.32 × 10−3, k2 = 120 − [250 × LL(L)], and k3 = 7.01.
- G—heat flux below the canopy (MJ m−2),
- LL(L)—lower limit of plant-available water in the soil layer (cm3 cm−3),
- —net radiation above the canopy (MJ m−2),
- —net radiation over the bare soil and residue (MJ m−2),
- RWU(L)—potential root uptake per unit root length for soil layer L (cm3watercm−3 root),
- RLV(L)—root length density in the soil layer (cm root cm−3 soil),
- SW(L)—current soil water content in the soil layer (cm3 cm−3),
- —potential transpiration (cm), calculated using the Shuttleworth–Wallace equation,
- —air vapor pressure deficit at the mean canopy height (kPa),
- —bulk boundary layer resistance of the canopy elements within the canopy (s m−1),
- —bulk stomatal resistance of the canopy (s m−1),
- —the psychrometric constant (kPa °C−1),
- —the density of air (kg m−3),
- —slope of the saturation vapor press versus temperature curve (kPa °C−1),
- —the depth of the soil layer (cm).
2.4. Irrigation Control Hardware
2.4.1. The F-PLC Design
2.4.2. The S-PLC Design
- to provide a touch screen for the user to implement irrigation events by controlling the electromagnetic valves and view real-time information on θ, and
- to control the F-PLC, which controls the circulating pump. The S-PLC mainly includes a touch screen (SIEMENS SMART LINE), PLC, and power source box.
- to monitor real time variation in θ for each plot in order to set an appropriate irrigation threshold under the desired irrigation regime, and to control the irrigation system for that plot, and
- to select manual or rotation irrigation mode.
2.4.3. The FCC Design
2.5. Experimental Site
2.6. Irrigation Treatment Design
- the DSSIS system using RZWQM2 model simulated water stress factor (D), soil moisture sensor (S), and experience (E).
- The other factor is irrigation amount, including full (FI) and deficit (DI, 75% of full) irrigation.
2.7. Yield, Water Productivity (WP), and Soil Water Content
- WP—irrigation water use efficiency (kg m−3),
- Y—seed cotton yield (kg m−2),
- I—the irrigation amount (m),
- P—precipitation (m).
2.8. Statistical and Economic Analysis
3. Results and Discussion
3.1. Effectiveness of Irrigation Scheduling Methods
3.2. Simulated and Measured Grain Yield and the Soil Moisture
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Navarro-Hellín, H.; Martínez-del-Rincon, J.; Domingo-Miguel, R.; Soto-Valles, F.; Torres-Sánchez, R. A decision support system for managing irrigation in agriculture. Comput. Electron. Agric. 2016, 124, 121–131. [Google Scholar] [CrossRef] [Green Version]
- Yang, G.; Liu, L.; Guo, P.; Li, M. A flexible decision support system for irrigation scheduling in an irrigation district in China. Agric. Water Manag. 2017, 179, 378–389. [Google Scholar] [CrossRef]
- Kong, Q. Analysis on the key factor of inhibiting cotton production development and discussion on its strategies. Xinjiang Agric. Sci. 2010, 47, 3–5. [Google Scholar]
- Wang, W.; Lu, J.; Ren, T.; Li, X.; Su, W.; Lu, M. Evaluating regional mean optimal nitrogen rates in combination with indigenous nitrogen supply for rice production. Field Crops Res. 2012, 137, 37–48. [Google Scholar] [CrossRef]
- Ghazichaki, Z.; Monem, M. Development of Quantified Model for Application of Control Systems in Irrigation Networks by System Dynamic Approach. Irrig. Drain. 2019, 68, 433–442. [Google Scholar] [CrossRef]
- Farooq, M.; Hussain, M.; Ul-Allah, S.; Siddique, K. Physiological and agronomic approaches for improving water-use efficiency in crop plants. Agric. Water Manag. 2019, 219, 95–108. [Google Scholar] [CrossRef]
- Ahmadi, S.; Andersen, M.; Plauborg, F.; Poulsen, R.; Jensen, C.; Sepaskhah, A.; Hansen, S. Effects of irrigation strategies and soils on field grown potatoes: Yield and water productivity. Agric. Water Manag. 2010, 97, 1923–1930. [Google Scholar] [CrossRef]
- Nawandar, N.; Satpute, V. IoT based low cost and intelligent module for smart irrigation system. Comput. Electron. Agric. 2019, 162, 979–990. [Google Scholar] [CrossRef]
- Kropp, I.; Nejadhashemi, A.; Deb, K.; Abouali, M.; Roy, P.; Adhikari, U.; Hoogenboom, G. A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification. Agric. Syst. 2019, 173, 289–302. [Google Scholar] [CrossRef]
- Ge, Y.; Li, X.; Huang, C.; Nan, Z. A Decision Support System for irrigation water allocation along the middle reaches of the Heihe River Basin, Northwest China. Environ. Model. Softw. 2013, 47, 182–192. [Google Scholar] [CrossRef]
- Grassini, P.; Yang, H.; Irmak, S.; Thorburn, J.; Burr, C.; Cassman, K. High-yield irrigated maize in the Western US Corn Belt: II. Irrigation management and crop water productivity. Field Crops Res. 2011, 120, 133–141. [Google Scholar] [CrossRef]
- Baseca, C.C.; Sendra, S.; Lloret, J.; Tomas, J.A. Smart Decision System for Digital Farming. Agronomy 2019, 9, 216. [Google Scholar] [CrossRef]
- Kinzli, K.; Gensler, D.; DeJonge, K.; Oad, R.; Shafike, N. Validation of a Decision Support System for Improving Irrigation System Performance. J. Irrig. Drain Eng. 2015, 141. [Google Scholar] [CrossRef]
- Dabach, S.; Lazarovitch, N.; Šimůnek, J.; Shani, U. Numerical investigation of irrigation scheduling based on soil water status. Irrig. Sci. 2013, 31, 27–36. [Google Scholar] [CrossRef]
- Tanure, S.; Nabinger, C.; Becker, J. Bioeconomic model of decision support system for farm management. Part I: Systemic conceptual modeling. Agric. Syst. 2013, 115, 104–116. [Google Scholar] [CrossRef]
- Dukes, M.; Scholberg, J. Soil moisture controlled subsurface drip irrigation on sandy soils. Appl. Eng. Agric. 2005, 21, 89–101. [Google Scholar] [CrossRef]
- Soulis, K.; Elmaloglou, S.; Dercas, N. Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems. Agric. Water Manag. 2015, 148, 258–268. [Google Scholar] [CrossRef]
- Car, N.; Christen, E.; Hornbuckle, J.; Moore, G. Using a mobile phone Short Messaging Service (SMS) for irrigation scheduling in Australia–Farmers’ participation and utility evaluation. Comput. Electron. Agric. 2012, 84, 132–143. [Google Scholar] [CrossRef]
- Blonquist, J.; Jones, S.; Robinson, D. Precise irrigation scheduling for turfgrass using a subsurface electromagnetic soil moisture sensor. Agric. Water Manag. 2006, 84, 153–165. [Google Scholar] [CrossRef]
- Coates, R.; Delwiche, M.; Brown, P. Design of a system for individual microsprinkler control. Trans. ASABE 2006, 49, 1963–1970. [Google Scholar] [CrossRef]
- Kim, Y.; Evans, R. Software design for wireless sensor-based site-specific irrigation. Comput. Electron. Agric. 2009, 66, 159–165. [Google Scholar] [CrossRef]
- Miller, G.; Farahani, H.; Hassell, R.; Khalilian, A.; Adelberg, J.; Wells, C. Field evaluation and performance of capacitance probes for automated drip irrigation of watermelons. Agric. Water Manag. 2014, 131, 124–134. [Google Scholar] [CrossRef]
- Taghvaeian, S.; Chávez, J.; Hansen, N. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in Northeastern Colorado. Remote Sens. 2012, 4, 3619–3637. [Google Scholar] [CrossRef]
- DeJonge, K.; Taghvaeian, S.; Trout, T.; Comas, L. Comparison of canopy temperature-based water stress indices for maize. Agric. Water Manag. 2015, 156, 51–62. [Google Scholar] [CrossRef]
- Carroll, D.; Hansen, N.; Hopkins, B.; DeJonge, K. Leaf temperature of maize and crop water stress index with variable irrigation and nitrogen supply. Irrig. Sci. 2017, 2, 1–12. [Google Scholar] [CrossRef]
- Gao, F.; Yu, L.; Zhang, W.; Xu, Q.; Jiang, Q. Preliminary study on precision irrigation system based on wireless sensor networks of acoustic emission for crop water stress. Trans. CSAE 2008, 24, 60–65. [Google Scholar]
- Playan, E.; Salvador, R.; Lopetz, C.; Lecina, S.; Dechmi, F.; Zapata, N. Solid-setsprinkler irrigation controllers driven by simulation models: Opportunities and bottlenecks. J. Irrigat. Drain. Eng. 2014, E140, 04013001. [Google Scholar] [CrossRef]
- O’Shaughnessy, S.; Evett, S.; Colaizzi, P.; Howell, T. Grain sorghum response to irrigation scheduling with the time-temperature threshold method and deficit irrigation levels. Trans. ASAE 2012, 55, 451–461. [Google Scholar] [CrossRef]
- Qi, Z.; Ma, L.; Bausch, W.; Trout, T.; Ahuja, L.; Flerchinger, G.; Fang, Q. Simulating maize production, water and energy balance, canopy temperature, and water stress under full and deficit irrigated corn. Trans. ASAE 2016, 59, 623–633. [Google Scholar]
- Thorp, K.; DeJonge, K.; Kaleita, A.; Batchelor, W.; Paz, J. Methodology for the use of DSSAT models for precision agriculture decision support. Comput. Electron. Agric. 2008, 64, 276–285. [Google Scholar] [CrossRef] [Green Version]
- Jiang, Y.; Xu, X.; Huang, Q.; Huo, Z.; Huang, G. Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model. Agric. Water Manag. 2016, 178, 76–88. [Google Scholar] [CrossRef] [Green Version]
- Thorp, K.; Hunsaker, D.; Bronson, K.; Andrade-Sanchez, P.; Barnes, E. Cotton irrigation scheduling using a crop growth model and FAO-56 methods: Field and simulation studies. Trans. ASABE 2017, 60, 2023–2039. [Google Scholar] [CrossRef]
- Kisekka, I.; Aguilar, J.P.; Rogers, D.H.; Holman, J.; O’Brien, D.M.; Klocke, N. Assessing deficit irrigation strategies for corn using simulation. Trans. ASAE 2016, 59, 303–317. [Google Scholar]
- Stulina, G.; Cameira, M.; Pereira, L. Using RZWQM to search improved practices for irrigated maize in Ferhana, Uzabekistan. Agric. Water Manag. 2005, 77, 263–281. [Google Scholar] [CrossRef]
- Cameira, M.; Fernando, R.; Ahuja, L. Using RZWQM to simulate the fate of nitrogen in field soil-crop environment in the Mediterranean region. Agric. Water Manag. 2007, 90, 121–136. [Google Scholar] [CrossRef]
- Hanson, J.; Ahuja, L.; Shaffer, M.; Rojas, K.; DeCoursey, D.; Farahani, H.; Johnson, K. RZWQM simulating the effects of management on water quality and crop production. Agric. Syst. 1998, 57, 161–195. [Google Scholar] [CrossRef]
- Saseendran, S.; Ahuja, L.; Ma, L.; Trout, T.; Andales, A.; Chaves, J.; Ham, J. Enhancing the water stress factors for simulation of corn (Zea mays L.) in RZWQM2. Agron. J. 2014, 106, 81–94. [Google Scholar] [CrossRef]
- Ma, L.; Trout, T.; Ahuja, L.; Bausch, W.; Saseendran, S.; Malone, R.; Nielsen, D. Calibrating RZWQM2 Model for maize responses to deficit irrigation. Agric. Water Manag. 2012, 103, 140–149. [Google Scholar] [CrossRef]
- Fang, Q.; Wang, J.; Yu, S. Water-saving potential and irrigation strategies for wheat-maize double cropping system in the North China Plain. Trans. CSAE 2011, 27, 37–44. [Google Scholar]
- Gu, Z.; Qi, Z.; Ma, L.; Gui, D.; Xu, J.; Fang, Q.; Yuan, S. Development of an irrigation scheduling software based on model predicted crop water stress. Comput. Electron. Agric. 2017, 143, 208–221. [Google Scholar] [CrossRef]
- Liu, C.; Qi, Z.; Gu, Z.; Gui, D.; Zeng, F. Optimizing irrigation rates for cotton production in an extremely arid area using RZEWM2-simulated water stress. Trans. ASABE 2017, 60, 1–14. [Google Scholar] [CrossRef]
- Ahuja, L.R.; Rojas, K.W.; Hanson, J.D.; Shaffer, M.J.; Ma, L. Root Zone Water Quality Model: Modeling Management Effects on Water Quality and Crop Production; Water Resources Publications: Littleton, CO, USA, 2000. [Google Scholar]
- Ma, L.; Hoogenboom, G.; Ahuja, L.; Ascough, J.; Saseendran, S.A. Evaluation of the RZWQM-CERES-Maize hybrid model for maize production. Agric. Syst. 2006, 87, 274–295. [Google Scholar] [CrossRef]
- Ma, L.; Hoogenboom, G.; Ahuja, L.; Nielsen, D.; Ascough, J.C. Development and evaluation of the RZWQM-CROPGRO hybrid model for soybean production. Agron. J. 2005, 97, 1172–1182. [Google Scholar] [CrossRef]
- Saseendran, S.; Trout, T.; Ahuja, L.; Ma, L.; McMaster, G.; Nielsen, D.; Andales, A.; Chavez, J.; Ham, J. Quantifying crop water stress factors from soil water measurements in a limited irrigation experiment. Agric. Syst. 2015, 137, 191–205. [Google Scholar] [CrossRef]
- Zotarelli, L.; Dukes, M.; Morgan, K. Interpretation of soil moisture content to determine soil field capacity and avoid over-irrigating sandy soils using soil moisture sensors. Agri. Bio. Eng. Dep. UF/IFAS Ext. 2013, AE460, 1–4. [Google Scholar]
- Haley, M.; Dukes, M. Validation of landscape irrigation reduction with soil moisture sensor irrigation controllers. J. Irrig. Drain Eng. 2012, 138, 135–144. [Google Scholar] [CrossRef]
- Shareef, M.; Gui, D.; Zeng, F.; Waqas, M.; Zhang, B.; Iqbal, H. Water productivity, growth, and physiological assessment of deficit irrigated cotton on hyperarid desert–oases in northwest China. Agric. Water Manag. 2018, 206, 1–10. [Google Scholar] [CrossRef]
- Ibragimov, N.; Evett, S.; Esanbekov, Y.; Kamilov, B.; Mirzaev, L.; Lamers, J. Water use efficiency of irrigated cotton in Uzbekistan under drip and furrow irrigation. Agric. Water Manag. 2007, 90, 112–120. [Google Scholar] [CrossRef]
- Sezen, S.; Yazar, A.; Kapur, B.; Tekin, S. Comparison of drip and sprinkler irrigation strategies on sunflower seed and oil yield and quality under Mediterranean climatic conditions. Agric. Water Manag. 2011, 98, 1153–1161. [Google Scholar] [CrossRef]
- Martins, J.D.; Rodrigues, G.C.; Paredes, P.; Carlesso, R.; Oliveira, Z.B.; Knies, A.E.; Petry, M.T.; Pereira, L.S. Dual crop coefficients for maize in southern Brazil: Model testing for sprinkler and drip irrigation and mulched soil. Biosyst. Eng. 2013, 115, 291–310. [Google Scholar] [CrossRef]
- Dar, E.; Brar, A.; Mishra, S.; Singh, K. Simulating response of wheat to timing and depth of irrigation water in drip irrigation system using CERES-wheat model. Field Crops Res. 2017, 214, 149–163. [Google Scholar] [CrossRef]
- Ma, L.; Nielsen, D.; Ahuja, L.; Malone, R.; Saseendran, S.; Rojas, K.; Hanson, J.; Benjamin, J. Evaluation of RZWQM under varying irrigation levels in eastern Colorado. Trans. ASAE 2003, 46, 39–49. [Google Scholar]
Treatment | Basis of Irrigation | Measurement | Irrigation Threshold | |
---|---|---|---|---|
Start | Stop | |||
D-FI | RZWQM2 model | SWFAC * | SWFAC < 0.9 | θfc |
D-DI | RZWQM2 model | SWFAC | Same as D-FI | 75% of D-FI |
S-FI | Soil moisture | Field-measured θ | θ0–0.1 m = 0.06 cm3 cm−3 | θ0–0.2 m = 0.15 cm3 cm−3 |
S-DI | Soil moisture | Field-measured θ | Same as S-FI | 75% of S-FI |
E-FI | Experience | Visual/tactile | Grower’s experience | θfc |
E-DI | Experience | Visual/Tactile | Same as E-FI | 75% of E-FI |
Factors | Seed Cotton Yield (Mg ha−1) | Total Irrigation Amount (mm) | WP (kg m−3) | ||||||
---|---|---|---|---|---|---|---|---|---|
2016 | 2017 | Average | 2016 | 2017 | Average | 2016 | 2017 | Average | |
Irrigation Scheduling | |||||||||
DSSIS (D) | 4.44 | 4.43 | 4.44A | 324 | 326 | 325B | 1.23 | 1.10 | 1.16A |
Sensor (S) | 4.00 | 2.80 | 3.40B | 429 | 204 | 316B | 0.86 | 0.98 | 0.92B |
Experience (E) | 3.40 | 4.02 | 3.71AB | 481 | 471 | 476A | 0.66 | 0.74 | 0.70C |
Irrigation amount | |||||||||
Full (FI) | 4.16 | 4.21 | 4.19a | 470 | 378 | 424a | 0.84 | 0.96 | 0.95a |
Deficit (DI) | 3.74 | 3.29 | 3.52b | 353 | 289 | 320b | 0.98 | 0.91 | 0.90a |
Treatments | Seed Cotton Yield (Mg ha−1) | Total Irrigation Amount (mm) | WP (kg m−3) | ||||||
---|---|---|---|---|---|---|---|---|---|
2016 | 2017 | Average | 2016 | 2017 | Average | 2016 | 2017 | Average | |
D-FI | 4.58 | 4.76 | 4.67a | 370 | 365 | 368c | 1.11 | 1.07 | 1.09ab |
D-DI | 4.30 | 4.10 | 4.20ab | 278 | 287 | 282d | 1.35 | 1.13 | 1.24a |
S-FI | 4.28 | 3.41 | 3.84abc | 490 | 234 | 362c | 0.80 | 1.09 | 0.95bc |
S-DI | 3.73 | 2.19 | 2.96c | 368 | 173 | 270e | 0.91 | 0.87 | 0.89bcd |
E-FI | 3.61 | 4.47 | 4.04abc | 550 | 537 | 543a | 0.61 | 0.73 | 0.67d |
E-DI | 3.20 | 3.58 | 3.39bc | 413 | 405 | 409b | 0.70 | 0.74 | 0.72cd |
Parameter a | Full Irrigation b | Deficit Irrigation b | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sim. | Obs. | MD | ENS | R2 | RMSE | Sim. | Obs. | MD | ENS | R2 | RMSE | |
2016–2017 | ||||||||||||
Grain yield (Mg ha−1) | 3.40 | 2.91 | 0.49 | — | — | — | 2.44 | 2.39 | 0.05 | — | — | — |
θ (0.05–0.15 m) | 0.13 | 0.13 | 0.006 | 0.55 | 0.61 | 0.033 | 0.12 | 0.12 | −0.002 | 0.69 | 0.69 | 0.031 |
θ (0.15–0.25 m) | 0.13 | 0.12 | 0.003 | 0.85 | 0.79 | 0.023 | 0.12 | 0.12 | −0.004 | 0.60 | 0.62 | 0.031 |
θ (0.25–0.40 m) | 0.13 | 0.12 | 0.012 | 0.65 | 0.71 | 0.030 | 0.10 | 0.10 | −0.001 | 0.46 | 0.47 | 0.030 |
θ (0.40–0.65 m) | 0.11 | 0.12 | −0.008 | −0.04 | 0.19 | 0.037 | 0.08 | 0.10 | −0.014 | −0.17 | 0.12 | 0.032 |
θ (0.65–1.00 m) | 0.10 | 0.11 | −0.013 | 0.12 | 0.39 | 0.024 | 0.09 | 0.09 | −0.003 | 0.19 | 0.20 | 0.203 |
SWS (0–1.00 m) | 10.86 | 10.29 | 0.572 | 0.48 | 0.60 | 1.879 | 8.85 | 9.25 | −0.407 | 0.48 | 0.51 | 1.896 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, X.; Qi, Z.; Gui, D.; Gu, Z.; Ma, L.; Zeng, F.; Li, L.; Sima, M.W. A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity. Agronomy 2019, 9, 686. https://doi.org/10.3390/agronomy9110686
Chen X, Qi Z, Gui D, Gu Z, Ma L, Zeng F, Li L, Sima MW. A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity. Agronomy. 2019; 9(11):686. https://doi.org/10.3390/agronomy9110686
Chicago/Turabian StyleChen, Xiaoping, Zhiming Qi, Dongwei Gui, Zhe Gu, Liwang Ma, Fanjiang Zeng, Lanhai Li, and Matthew W. Sima. 2019. "A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity" Agronomy 9, no. 11: 686. https://doi.org/10.3390/agronomy9110686