TY - GEN
T1 - Sensor-based irrigation of Maize and Soybean in East-Central Nebraska under a sub-humid climate
AU - Singh, Jasreman
AU - Heeren, Derek M.
AU - Ge, Yufeng
AU - Bai, Geng
AU - Neale, Christopher M.U.
AU - Maguire, Mitchell S.
AU - Bhatti, Sandeep
N1 - Funding Information:
The funding for this research was provided by a grant from the USDA NIFA Agricultural and Food Research Initiative (Award Number 2017-67021-26249) and Graduate Student Support from the Robert B. Daugherty Water for Food Global Institute at the University of Nebraska. Additional support was received from the Hatch Act (USDA NIFA, Accession Number 1009760) and the Department of Biological Systems Engineering at the University of Nebraska-Lincoln. The authors thank Dr. Burdette Barker for input in the experimental design; Alan L. Boldt, Eric Wilkening, and Suresh Pradhyun Kashyap for assistance with the data collection; Mr. Mark Schroeder and his team from the University of Nebraska’s Eastern Nebraska Research and Extension Center for their cooperation and help with field operations. Weather data were provided by the Nebraska Mesonet and the Nebraska State Climate Office through the High Plains Regional Climate Center.
Publisher Copyright:
© American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - The ever increasing pressure on the water resources in Nebraska and other irrigated agricultural areas require innovations and solutions for the governance of water allocation. This study proposes the use of sensor-based method for irrigation which has the potential to improve irrigation water use efficiency (IWUE). Practical methods and algorithms for creating irrigation prescriptions have become vital for the adoption of precision irrigation. A decision support system (DSS) for uniform irrigation was evaluated during 2020 growing season in a sub-humid region. The DSS was managed using soil water and plant feedback. In field practice, a sensor node station comprising of soil water content sensors and infrared thermometer (IRT) was installed in maize and soybean. Root zone water depletion (Drw), and crop water stress index (CWSI) served as the inputs for soil water, and plant feedback, respectively. The timing and depth of irrigation was determined using the DSS. The results of the sensor-based DSS treatment were compared to conventional treatment (managed by a crop consultant) and rainfed (no-irrigation) treatment. Test results for maize and soybean indicated that there was no significant difference in crop yield between sensor-based and conventional treatments. However, the sensor-based DSS treatment witnessed higher IWUE for both maize and soybean. The observed yield for rainfed treatment was significantly lower than the irrigated treatments in maize and soybean. There is a great potential for the use of this DSS system for uniform irrigation in humid and sub-humid regions and future studies are required for the adoption of this technology.
AB - The ever increasing pressure on the water resources in Nebraska and other irrigated agricultural areas require innovations and solutions for the governance of water allocation. This study proposes the use of sensor-based method for irrigation which has the potential to improve irrigation water use efficiency (IWUE). Practical methods and algorithms for creating irrigation prescriptions have become vital for the adoption of precision irrigation. A decision support system (DSS) for uniform irrigation was evaluated during 2020 growing season in a sub-humid region. The DSS was managed using soil water and plant feedback. In field practice, a sensor node station comprising of soil water content sensors and infrared thermometer (IRT) was installed in maize and soybean. Root zone water depletion (Drw), and crop water stress index (CWSI) served as the inputs for soil water, and plant feedback, respectively. The timing and depth of irrigation was determined using the DSS. The results of the sensor-based DSS treatment were compared to conventional treatment (managed by a crop consultant) and rainfed (no-irrigation) treatment. Test results for maize and soybean indicated that there was no significant difference in crop yield between sensor-based and conventional treatments. However, the sensor-based DSS treatment witnessed higher IWUE for both maize and soybean. The observed yield for rainfed treatment was significantly lower than the irrigated treatments in maize and soybean. There is a great potential for the use of this DSS system for uniform irrigation in humid and sub-humid regions and future studies are required for the adoption of this technology.
KW - Crop water stress index
KW - Decision support system
KW - Irrigation water use efficiency
KW - Root zone water depletion
KW - Sub-humid environment
KW - Uniform irrigation
UR - http://www.scopus.com/inward/record.url?scp=85114201388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114201388&partnerID=8YFLogxK
U2 - 10.13031/aim.202101044
DO - 10.13031/aim.202101044
M3 - Conference contribution
AN - SCOPUS:85114201388
T3 - American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021
SP - 2416
EP - 2427
BT - American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021
PB - American Society of Agricultural and Biological Engineers
T2 - 2021 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021
Y2 - 12 July 2021 through 16 July 2021
ER -